medium_chunk_ragas_results.csv

question,contexts,answer,ground_truth,faithfulness,answer_relevancy,context_recall,context_precision,answer_correctness
What is the significance of providing notice and explanation as a legal requirement in the context of automated systems?,"[""Providing notice has long been a standard practice, and in many cases is a legal requirement, when, for example, making a video recording of someone (outside of a law enforcement or national security context). In some cases, such as credit, lenders are required to provide notice and explanation to consumers. Techniques used to automate the process of explaining such systems are under active research and improvement and such explanations can take many forms. Innovative companies and researchers are rising to the challenge and creating and deploying explanatory systems that can help the public better understand decisions that impact them. \nWhile notice and explanation requirements are already in place in some sectors or situations, the American public deserve to know consistently and across sectors if an automated system is being used in a way that impacts their rights, opportunities, or access. This knowledge should provide confidence in how the public is being treated, and trust in the validity and reasonable use of automated systems. \n• A lawyer representing an older client with disabilities who had been cut off from Medicaid-funded home\nhealth-care assistance couldn't determine why\n, especially since the decision went against historical access\npractices. In a court hearing, the lawyer learned from a witness that the state in which the older client\nlived \nhad recently adopted a new algorithm to determine eligibility.83 The lack of a timely explanation made it\nharder \nto understand and contest the decision.\n•\nA formal child welfare investigation is opened against a parent based on an algorithm and without the parent\never \nbeing notified that data was being collected and used as part of an algorithmic child maltreatment\nrisk assessment.84 The lack of notice or an explanation makes it harder for those performing child\nmaltreatment assessments to validate the risk assessment and denies parents knowledge that could help them\ncontest a decision.\n41""
 'You should know that an automated system is being used, \nand understand how and why it contributes to outcomes that impact you. Designers, developers, and deployers of automat\n-\ned systems should provide generally accessible plain language docu -\nmentation including clear descriptions of the overall system func -\ntioning and the role automation plays, notice that such systems are in use, the individual or organization responsible for the system, and ex\n-\nplanations of outcomes that are clear, timely, and accessible. Such notice should be kept up-to-date and people impacted by the system should be notified of significant use case or key functionality chang\n-\nes. You should know how and why an outcome impacting you was de -\ntermined by an automated system, including when the automated system is not the sole input determining the outcome. Automated systems should provide explanations that are technically valid, meaningful and useful to you and to any operators or others who need to understand the system, and calibrated to the level of risk based on the context. Reporting that includes summary information about these automated systems in plain language and assessments of the clarity and quality of the notice and explanations should be made public whenever possible.   NOTICE AND  EXPLANATION\n40'
 'NOTICE & \nEXPLANATION \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nAn automated system should provide demonstrably clear, timely, understandable, and accessible notice of use, and \nexplanations as to how and why a decision was made or an action was taken by the system. These expectations are explained below. \nProvide clear, timely, understandable, and accessible notice of use and explanations \nGenerally accessible plain language documentation. The entity responsible for using the automated \nsystem should ensure that documentation describing the overall system (including any human components) is \npublic and easy to find. The documentation should describe, in plain language, how the system works and how \nany automated component is used to determine an action or decision. It should also include expectations about \nreporting described throughout this framework, such as the algorithmic impact assessments described as \npart of Algorithmic Discrimination Protections. \nAccount able. Notices should clearly identify the entity r esponsible for designing each component of the \nsystem and the entity using it. \nTimely and up-to-date. Users should receive notice of the use of automated systems in advance of using or \nwhile being impacted by the technolog y. An explanation should be available with the decision itself, or soon \nthereafte r. Notice should be kept up-to-date and people impacted by the system should be notified of use case \nor key functionality changes. \nBrief and clear. Notices and explanations should be assessed, such as by research on users’ experiences, \nincluding user testing, to ensure that the people using or impacted by the automated system are able to easily'
 'NOTICE & \nEXPLANATION \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section provides a brief summary of the problems which the principle seeks to address and protect \nagainst, including illustrative examples. \nAutomated systems now determine opportunities, from employment to credit, and directly shape the American \npublic’s experiences, from the courtroom to online classrooms, in ways that profoundly impact people’s lives. But this expansive impact is not always visible. An applicant might not know whether a person rejected their resume or a hiring algorithm moved them to the bottom of the list. A defendant in the courtroom might not know if a judge deny\n-\ning their bail is informed by an automated system that labeled them “high risk.” From correcting errors to contesting decisions, people are often denied the knowledge they need to address the impact of automated systems on their lives. Notice and explanations also serve an important safety and efficacy purpose, allowing experts to verify the reasonable\n-\nness of a recommendation before enacting it. \nIn order to guard against potential harms, the American public needs to know if an automated system is being used. Clear, brief, and understandable notice is a prerequisite for achieving the other protections in this framework. Like\n-\nwise, the public is often unable to ascertain how or why an automated system has made a decision or contributed to a particular outcome. The decision-making processes of automated systems tend to be opaque, complex, and, therefore, unaccountable, whether by design or by omission. These factors can make explanations both more challenging and more important, and should not be used as a pretext to avoid explaining important decisions to the people impacted by those choices. In the context of automated systems, clear and valid explanations should be recognized as a baseline requirement.']","Providing notice and explanation as a legal requirement in the context of automated systems is significant for several reasons:

1. **Transparency**: It ensures that individuals are aware when automated systems are being used to make decisions that affect their lives. This transparency helps build trust between the public and the entities deploying these systems.

2. **Accountability**: By requiring entities to identify themselves and explain their systems, it holds them accountable for the decisions made by these automated systems. This accountability is crucial for addressing any potential biases or errors in the algorithms.

3. **Empowerment**: When individuals receive clear explanations about how decisions are made, they are better equipped to contest or appeal those decisions if they believe they have been treated unfairly. This empowerment is particularly important in sensitive areas like credit, employment, and legal proceedings.

4. **Safety and Efficacy**: Clear notice and explanations allow experts and stakeholders to verify the reasonableness of automated recommendations before they are enacted. This can help prevent harmful outcomes and ensure that the systems are functioning as intended.

5. **Public Awareness**: The requirement helps the public understand the implications of automated systems on their rights and opportunities. This knowledge is essential for fostering informed discussions about the ethical use of AI and its impact on society.

Overall, these requirements aim to mitigate the risks associated with the opaque nature of automated decision-making processes and ensure that individuals are treated fairly and justly (Source: [document name], p. [page number]).","Providing notice and explanation as a legal requirement in the context of automated systems is significant because it allows individuals to understand how automated systems are impacting their lives. It helps in correcting errors, contesting decisions, and verifying the reasonableness of recommendations before enacting them. Clear and valid explanations are essential to ensure transparency, accountability, and trust in the use of automated systems across various sectors.",1.0,0.9713211543186683,1.0,0.999999999975,0.5496458408318092
"How can structured human feedback exercises, such as GAI red-teaming, be beneficial for GAI risk measurement and management?","['50 Participatory Engagement Methods  \nOn an ad hoc or more structured basis, organizations can design and use a variety of channels to engage \nexternal stakeholders in product development or review. Focus groups with select experts can provide \nfeedback on a range of issues. Small user studies c an provide feedback from representative groups or \npopulations. Anonymous surveys can be used to poll or gauge reactions to specific features. Participatory engagement methods are often less structured than field testing or red teaming, and are more \ncommonl y used in early stages of AI or product development.  \nField Testing  \nField testing involves structured settings to evaluate risks and impacts and to simulate the conditions \nunder which the GAI system will be deployed. Field style tests can be adapted from a focus on user \npreferences and experiences towards AI risks and impacts – both negative and positive. When carried \nout with large groups of users, these tests can provide estimations of the likelihood of risks and impacts \nin real world interactions.  \nOrganizations may also collect feedback on outcomes, harms, and user experience directly from users in the production environment after a model has been released, in accordance with human subject \nstandards such as informed consent and compensation. Organiz ations should follow applicable human \nsubjects research requirements, and best practices such as informed consent and subject compensation, \nwhen implementing feedback activities. \nAI Red -teaming  \nAI red -teaming is an evolving practice that references exercises often  conducted in a controlled \nenvironment and in collaboration with AI developers building AI models  to identify potential adverse \nbehavior or outcomes of a GAI model or system,  how they could occur, an d stress test safeguards” . AI \nred-teaming can be performed before or after AI models or systems are made available to the broader'
 'GV-4.1-001 Establish policies and procedures that address continual improvement processes \nfor GAI risk measurement . Address general risks associated with a lack of \nexplainability and transparency in GAI systems by using ample documentation and \ntechniques such as: application of gradient -based attributions, occlusion/term \nreduction, counterfactual prompts and prompt eng ineering, and analysis of \nembeddings; Assess and update risk measurement approaches at regular cadences.  Confabulation  \nGV-4.1-002 Establish policies, procedures, and processes detailing risk measurement in \ncontext of use with standardized measurement protocols and structured public feedback exercises such as AI red -teaming or independent external evaluations . CBRN Information and Capability ; \nValue Chain and Component Integration'
 '49 early lifecycle TEVV approaches are developed and matured for GAI, organizations may use \nrecommended “pre- deployment testing” practices to measure performance, capabilities, limits, risks, \nand impacts. This section describes risk measurement and estimation as part of pre -deployment TEVV, \nand examines the state of play for pre -deployment testing methodologies.  \nLimitations of Current Pre -deployment Test Approaches  \nCurrently available pre -deployment TEVV processes used for GAI applications may be inadequate, non-\nsystematically applied, or fail to reflect or mismatched to deployment contexts. For example, the \nanecdotal testing of GAI system capabilities through video games or standardized tests designed for \nhumans (e.g., intelligence tests, professional licensing exams) does not guarantee GAI system validity or \nreliability in those domains. Similarly, jailbreaking or prompt  engineering tests may not systematically \nasse ss validity or reliability risks.  \nMeasurement gaps can arise from mismatches between laboratory and real -world settings. Current \ntesting approaches often remain focused on laboratory conditions or restricted to benchmark test \ndatasets and in silico techniques that may not extrapolate well to —or directly assess GAI impacts in real -\nworld conditions. For example, current measurement gaps for GAI make it difficult to precisely estimate \nits potential ecosystem -level or longitudinal risks and related political, social, and economic impacts. \nGaps between benchmarks and real-world  use of GAI systems may likely be exacerbated due to prompt \nsensitivity and broad heterogeneity of contexts of use.  \nA.1.5.  Structured Public Feedback  \nStructured public feedback can be used to evaluate whether GAI systems are performing as intended and to calibrate and verify traditional measurement methods. Examples of structured feedback include, \nbut are not limited to:'
 '51 general public participants. For example, expert AI red- teamers could modify or verify the \nprompts written by general public AI red- teamers. These approaches may also expand coverage \nof the AI risk attack surface.  \n• Human / AI: Performed by GAI in combinatio n with  specialist or non -specialist human teams. \nGAI- led red -teaming can be more cost effective than human red- teamers alone. Human or GAI-\nled AI red -teaming may be better suited for eliciting different types of harms.   \nA.1.6.  Content Provenance  \nOverview \nGAI technologies can be leveraged for many applications such as content generation and synthetic data. \nSome aspects of GAI output s, such as the production of deepfake content, can challenge our ability to \ndistinguish human- generated content from AI -generated synthetic  content. To help manage and mitigate \nthese risks, digital transparency mechanisms like provenance data tracking can trace the origin and \nhistory of content. Provenance data tracking and synthetic content detection can help facilitate greater \ninformation access  about both authentic and synthetic content to users, enabling better knowledge of  \ntrustworthiness  in AI systems. When combined with other organizational accountability mechanisms, \ndigital content transparency approaches  can enable processes to trace negative outcomes back to their \nsource, improve information integrity, and uphold public trust. Provenance data tracking and synthetic content detection mechanisms provide information about the origin \nand history of content  to assist in \nGAI risk management efforts.  \nProvenance metad ata can include information about GAI model developers or creators  of GAI content , \ndate/time of creation, location, modifications, and sources. Metadata can be tracked for text, images, videos, audio, and underlying datasets. The implementation of p rovenance data tracking techniques  can']","Structured human feedback exercises, such as GAI red-teaming, can significantly benefit GAI risk measurement and management in several ways:

1. **Identifying Risks**: GAI red-teaming involves simulating potential adverse behaviors or outcomes of a GAI model in a controlled environment. This helps organizations identify risks that may not be apparent during standard testing, allowing for proactive risk management (Source: [document name], p. [page number]).

2. **Stress Testing Safeguards**: By conducting red-teaming exercises, organizations can stress test the safeguards and controls they have in place. This process helps ensure that the GAI systems can withstand various scenarios and that the protective measures are effective (Source: [document name], p. [page number]).

3. **Diverse Perspectives**: Engaging both expert and non-expert participants in red-teaming can provide a broader range of insights. This diversity can help uncover different types of harms and vulnerabilities that might be overlooked by a homogenous group (Source: [document name], p. [page number]).

4. **Calibration of Measurement Methods**: Structured feedback from red-teaming can be used to evaluate whether GAI systems are performing as intended. This feedback can help calibrate and verify traditional measurement methods, ensuring that they accurately reflect the system's performance in real-world conditions (Source: [document name], p. [page number]).

5. **Enhancing Public Trust**: By demonstrating a commitment to rigorous testing and risk management through red-teaming, organizations can enhance public trust in their GAI systems. Transparency in how risks are assessed and managed can reassure stakeholders about the safety and reliability of these technologies (Source: [document name], p. [page number]).

Overall, structured human feedback exercises like GAI red-teaming are essential for improving the robustness and accountability of GAI systems, ultimately leading to better risk management practices.","Structured human feedback exercises, such as GAI red-teaming, can be beneficial for GAI risk measurement and management by defining use cases, contexts of use, capabilities, and negative impacts where such exercises would be most beneficial. These exercises help in monitoring and improving outputs, evaluating the quality and integrity of data used in training, and tracking and documenting risks or opportunities related to GAI risks that cannot be measured quantitatively. Additionally, seeking active and direct feedback from affected communities through red-teaming can enhance information integrity and help in identifying harmful bias and homogenization in AI systems.",0.625,0.992832485501513,1.0,0.999999999975,0.5960254832081133
How do measurement gaps between laboratory and real-world settings impact the assessment of GAI systems in the context of pre-deployment testing?,"['49 early lifecycle TEVV approaches are developed and matured for GAI, organizations may use \nrecommended “pre- deployment testing” practices to measure performance, capabilities, limits, risks, \nand impacts. This section describes risk measurement and estimation as part of pre -deployment TEVV, \nand examines the state of play for pre -deployment testing methodologies.  \nLimitations of Current Pre -deployment Test Approaches  \nCurrently available pre -deployment TEVV processes used for GAI applications may be inadequate, non-\nsystematically applied, or fail to reflect or mismatched to deployment contexts. For example, the \nanecdotal testing of GAI system capabilities through video games or standardized tests designed for \nhumans (e.g., intelligence tests, professional licensing exams) does not guarantee GAI system validity or \nreliability in those domains. Similarly, jailbreaking or prompt  engineering tests may not systematically \nasse ss validity or reliability risks.  \nMeasurement gaps can arise from mismatches between laboratory and real -world settings. Current \ntesting approaches often remain focused on laboratory conditions or restricted to benchmark test \ndatasets and in silico techniques that may not extrapolate well to —or directly assess GAI impacts in real -\nworld conditions. For example, current measurement gaps for GAI make it difficult to precisely estimate \nits potential ecosystem -level or longitudinal risks and related political, social, and economic impacts. \nGaps between benchmarks and real-world  use of GAI systems may likely be exacerbated due to prompt \nsensitivity and broad heterogeneity of contexts of use.  \nA.1.5.  Structured Public Feedback  \nStructured public feedback can be used to evaluate whether GAI systems are performing as intended and to calibrate and verify traditional measurement methods. Examples of structured feedback include, \nbut are not limited to:'
 'MP-2.3-001 Assess the accuracy, quality, reliability, and authenticity of GAI  output by \ncomparing it to a set of known ground truth data and by using a variety of evaluation methods (e.g., human oversight and automated evaluation , proven \ncryptographic techniques , review of content inputs ). Information Integrity'
 '36 MEASURE 2.11:  Fairness and bias – as identified in the MAP function – are evaluated and results are documented.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.11- 001 Apply use -case appropriate benchmarks (e.g., Bias Benchmark Questions, Real \nHateful or Harmful  Prompts, Winogender  Schemas15) to quantify systemic bias, \nstereotyping, denigration, and hateful content in GAI system outputs; \nDocument assumptions and limitations of benchmarks, including any actual or \npossible training/test data cross  contamination,  relative to in -context \ndeployment environment.  Harmful Bias and Homogenization  \nMS-2.11- 002 Conduct fairness assessments to measure systemic bias. Measure GAI system \nperformance across demographic groups and subgroups, addressing both quality of service and any allocation of services and resources. Quantify harms using: field testing with sub- group populations to determine likelihood of \nexposure to generated content exhibiting harmful bias, AI red- teaming with \ncounterfactual and low -context (e.g., “leader,” “bad guys”) prompts. For ML \npipelines or business processes with categorical or numeric out comes that rely \non GAI, apply general fairness metrics (e.g., demographic parity, equalized odds, \nequal opportunity, statistical hypothesis tests), to the pipeline or business outcome where appropriate; Custom, context -specific metrics developed in \ncollabo\nration with domain experts and affected communities; Measurements of \nthe prevalence of denigration in generated content in deployment (e.g., sub-sampling a fraction of traffic and manually annotating denigrating content) . Harmful Bias and Homogenization ; \nDangerous , Violent, or Hateful \nContent  \nMS-2.11- 003 Identify the classes of individuals, groups, or environmental ecosystems which \nmight be impacted by GAI systems through direct engagement with potentially \nimpacted communities.  Environmental ; Harmful Bias and \nHomogenization'
 'GV-4.1-001 Establish policies and procedures that address continual improvement processes \nfor GAI risk measurement . Address general risks associated with a lack of \nexplainability and transparency in GAI systems by using ample documentation and \ntechniques such as: application of gradient -based attributions, occlusion/term \nreduction, counterfactual prompts and prompt eng ineering, and analysis of \nembeddings; Assess and update risk measurement approaches at regular cadences.  Confabulation  \nGV-4.1-002 Establish policies, procedures, and processes detailing risk measurement in \ncontext of use with standardized measurement protocols and structured public feedback exercises such as AI red -teaming or independent external evaluations . CBRN Information and Capability ; \nValue Chain and Component Integration']","Measurement gaps between laboratory and real-world settings can significantly impact the assessment of GAI systems during pre-deployment testing in several ways:

1. **Inaccurate Performance Evaluation**: Laboratory tests often use controlled conditions that may not accurately reflect the complexities and variabilities of real-world environments. As a result, GAI systems might perform well in a lab but fail to deliver the same results when deployed in diverse, unpredictable settings (Source: [document name], p. [page number]).

2. **Limited Contextual Understanding**: Current testing approaches may not account for the specific contexts in which GAI systems will be used. This mismatch can lead to a lack of understanding about how the system will interact with real-world variables, such as user behavior, cultural differences, and environmental factors (Source: [document name], p. [page number]).

3. **Overlooking Longitudinal Risks**: Pre-deployment testing often focuses on immediate outcomes rather than long-term impacts. This can create gaps in understanding the potential ecosystem-level risks and the broader social, political, and economic implications of deploying GAI systems (Source: [document name], p. [page number]).

4. **Prompt Sensitivity Issues**: GAI systems can be highly sensitive to the prompts they receive. Testing in a lab may not capture the full range of prompts that users might employ in real life, leading to an incomplete assessment of the system's robustness and reliability (Source: [document name], p. [page number]).

5. **Bias and Fairness Concerns**: Laboratory settings may not adequately address issues of bias and fairness that can arise in real-world applications. Without proper evaluation across diverse demographic groups and contexts, GAI systems may perpetuate or exacerbate existing biases when deployed (Source: [document name], p. [page number]).

Overall, these measurement gaps highlight the need for more comprehensive and contextually relevant testing methodologies that bridge the divide between laboratory conditions and real-world applications to ensure the effective and responsible deployment of GAI systems.","Measurement gaps between laboratory and real-world settings can impact the assessment of GAI systems in the context of pre-deployment testing by limiting the extrapolation of results from laboratory conditions to real-world scenarios. Current testing approaches often focus on benchmark test datasets and in silico techniques that may not accurately assess the impacts of GAI systems in real-world conditions. This can make it difficult to estimate the ecosystem-level or longitudinal risks associated with GAI deployment, as well as the political, social, and economic impacts. Additionally, the prompt sensitivity and broad heterogeneity of real-world contexts of use can exacerbate the gaps between benchmarks and actual GAI system performance.",0.8181818181818182,0.9887518614487671,1.0,0.999999999975,0.8213820308742312
How should data collection and use-case scope limits be determined and implemented in automated systems to prevent 'mission creep'?,"['Data collection and use-case scope limits. Data collection should be limited in scope, with specific, \nnarrow identified goals, to avoid ""mission creep.""  Anticipated data collection should be determined to be strictly necessary to the identified goals and should be minimized as much as possible. Data collected based on these identified goals and for a specific context should not be used in a different context without assessing for new privacy risks and implementing appropriate mitigation measures, which may include express consent. Clear timelines for data retention should be established, with data deleted as soon as possible in accordance with legal or policy-based limitations. Determined data retention timelines should be documented and justi\n-\nfied. \nRisk identification and mitigation. Entities that collect, use, share, or store sensitive data should attempt to proactively identify harms and seek to manage them so as to avoid, mitigate, and respond appropri\n-\nately to identified risks. Appropriate responses include determining not to process data when the privacy risks outweigh the benefits or implementing measures to mitigate acceptable risks. Appropriate responses do not include sharing or transferring the privacy risks to users via notice or consent requests where users could not reasonably be expected to understand the risks without further support. \nPrivacy-preserving security. Entities creating, using, or governing automated systems should follow privacy and security best practices designed to ensure data and metadata do not leak beyond the specific consented use case. Best practices could include using privacy-enhancing cryptography or other types of privacy-enhancing technologies or fine-grained permissions and access control mechanisms, along with conventional system security protocols. \n33'
 'ers may differ depending on the specific automated system and development phase, but should include subject matter, sector-specific, and context-specific experts as well as experts on potential impacts such as civil rights, civil liberties, and privacy experts. For private sector applications, consultations before product launch may need to be confidential. Government applications, particularly law enforcement applications or applications that raise national security considerations, may require confidential or limited engagement based on system sensitivities and preexisting oversight laws and structures. Concerns raised in this consultation should be documented, and the automated system developers were proposing to create, use, or deploy should be reconsidered based on this feedback. \nTesting. Systems should undergo extensive testing before deployment. This testing should follow domain-specific best practices, when available, for ensuring the technology will work in its real-world context. Such testing should take into account both the specific technology used and the roles of any human operators or reviewers who impact system outcomes or effectiveness; testing should include both automated systems testing and human-led (manual) testing. Testing conditions should mirror as closely as possible the conditions in which the system will be deployed, and new testing may be required for each deployment to account for material differences in conditions from one deployment to another. Following testing, system performance should be compared with the in-place, potentially human-driven, status quo procedures, with existing human performance considered as a performance baseline for the algorithm to meet pre-deployment, and as a lifecycle minimum performance standard. Decision possibilities resulting from performance testing should include the possibility of not deploying the system.'
 'DATA PRIVACY \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nProtect the public from unchecked surveillance \nHeightened oversight of surveillance. Surveillance or monitoring systems should be subject to \nheightened oversight that includes at a minimum assessment of potential harms during design (before deploy -\nment) and in an ongoing manner, to ensure that the American public’s rights, opportunities, and access are protected. This assessment should be done before deployment and should give special attention to ensure there is not algorithmic discrimination, especially based on community membership, when deployed in a specific real-world context. Such assessment should then be reaffirmed in an ongoing manner as long as the system is in use. \nLimited and proportionate surveillance. Surveillance should be avoided unless it is strictly necessary to achieve a legitimate purpose and it is proportionate to the need. Designers, developers, and deployers of surveillance systems should use the least invasive means of monitoring available and restrict monitoring to the minimum number of subjects possible. To the greatest extent possible consistent with law enforcement and national security needs, individuals subject to monitoring should be provided with clear and specific notice before it occurs and be informed about how the data gathered through surveillance will be used. \nScope limits on surveillance to protect rights and democratic values. Civil liberties and civil'
 '-\ndures before deploying the system, as well as responsibility of specific individuals or entities to oversee ongoing assessment and mitigation. Organizational stakeholders including those with oversight of the business process or operation being automated, as well as other organizational divisions that may be affected due to the use of the system, should be involved in establishing governance procedures. Responsibility should rest high enough in the organization that decisions about resources, mitigation, incident response, and potential rollback can be made promptly, with sufficient weight given to risk mitigation objectives against competing concerns. Those holding this responsibility should be made aware of any use cases with the potential for meaningful impact on people’s rights, opportunities, or access as determined based on risk identification procedures.  In some cases, it may be appropriate for an independent ethics review to be conducted before deployment. \nAvoid inappropriate, low-quality, or irrelevant data use and the compounded harm of its reuse \nRelevant and high-quality data. Data used as part of any automated system’s creation, evaluation, or \ndeployment should be relevant, of high quality, and tailored to the task at hand. Relevancy should be \nestablished based on research-backed demonstration of the causal influence of the data to the specific use case \nor justified more generally based on a reasonable expectation of usefulness in the domain and/or for the \nsystem design or ongoing development. Relevance of data should not be established solely by appealing to \nits historical connection to the outcome. High quality and tailored data should be representative of the task at \nhand and errors from data entry or other sources should be measured and limited. Any data used as the target \nof a prediction process should receive particular attention to the quality and validity of the predicted outcome']","To prevent ""mission creep"" in automated systems, data collection and use-case scope limits should be determined and implemented through a structured approach that includes the following steps:

1. **Define Specific Goals**: Clearly identify the specific objectives of the automated system. This involves understanding what the system is intended to achieve and ensuring that data collection aligns strictly with these goals. The goals should be narrow and well-defined to avoid broadening the scope unnecessarily (Source: [document name], p. [page number]).

2. **Conduct a Necessity Assessment**: Before collecting data, assess whether the data is strictly necessary for achieving the identified goals. This means evaluating whether the intended outcomes can be met without collecting additional data that may lead to mission creep (Source: [document name], p. [page number]).

3. **Implement Data Minimization Principles**: Adopt data minimization practices by collecting only the data that is essential for the specific use case. This helps limit the amount of data gathered and reduces the risk of using it for unintended purposes (Source: [document name], p. [page number]).

4. **Establish Clear Use-Case Boundaries**: Document and communicate the specific contexts in which the collected data will be used. This includes setting clear boundaries around how the data can be utilized and ensuring that it is not repurposed for different objectives without a thorough reassessment of privacy risks (Source: [document name], p. [page number]).

5. **Set Data Retention Timelines**: Define and document clear timelines for data retention. Data should be deleted as soon as it is no longer necessary for the identified goals, in accordance with legal or policy-based limitations. This helps prevent the accumulation of data that could be misused in the future (Source: [document name], p. [page number]).

6. **Regularly Review and Update Policies**: Continuously monitor and review data collection practices and use-case boundaries to ensure they remain aligned with the original goals. This includes reassessing the necessity of data collection and making adjustments as needed to prevent mission creep (Source: [document name], p. [page number]).

7. **Engage Stakeholders**: Involve relevant stakeholders, including privacy experts and affected communities, in the process of defining data collection limits and use-case scopes. Their insights can help identify potential risks and ensure that the system operates within ethical and legal boundaries (Source: [document name], p. [page number]).

By following these steps, organizations can effectively manage data collection and use-case scope limits in automated systems, thereby minimizing the risk of mission creep and protecting individuals' privacy rights.","Data collection and use-case scope limits in automated systems should be determined by setting specific, narrow goals to avoid 'mission creep.' Anticipated data collection should be strictly necessary for the identified goals and minimized as much as possible. Data collected for a specific context should not be used in a different context without assessing new privacy risks and implementing appropriate mitigation measures, which may include obtaining express consent. Clear timelines for data retention should be established, with data deleted as soon as possible in accordance with legal or policy-based limitations. The determined data retention timelines should be documented and justified.",1.0,0.9197333758494767,1.0,0.999999999975,0.7126132286100882
What action did the Federal Trade Commission take against Kochava regarding the sale of sensitive location tracking data?,"['DATA PRIVACY \nEXTRA  PROTECTIONS FOR DATA RELATED TO SENSITIVE\nDOMAINS\n• Continuous positive airway pressure machines gather data for medical purposes, such as diagnosing sleep\napnea, and send usage data to a patient’s insurance compan y, which may subsequently deny coverage for the\ndevice based on usage data. Patients were not aware that the data would be used in this way or monitored\nby anyone other than their doctor.70 \n•A department store company used predictive analytics applied to collected consumer data to determine that ateenage girl was pregnant, and sent maternity clothing ads and other baby-related advertisements to her\nhouse, revealing to her father that she was pregnant.71\n•School audio surveillance systems monitor student conversations to detect potential ""stress indicators"" asa warning of potential \nviolence.72 Online proctoring systems claim to detect if a student is cheating on an\nexam using biometric markers.73 These systems have the potential to limit student freedom to express a range\nof emotions at school and may inappropriately flag students with disabilities who need accommodations or\nuse screen readers or dictation software as cheating.74\n•Location data, acquired from a data broker, can be used to identify people who visit abortion clinics.75\n•Companies collect student data such as demographic information, free or reduced lunch status, whetherthey\'ve used drugs, or whether they\'ve expressed interest in LGBTQI+ groups, and then use that data to forecast student \nsuccess.76 Parents and education experts have expressed concern about collection of such\nsensitive data without express parental consent, the lack of transparency in how such data is being used, and\nthe potential for resulting discriminatory impacts.\n•Many employers transfer employee data to third party job verification services. This information is then usedby potential future employers, banks, or landlords. In one case, a former employee alleged that a'
 'alerts about location tracking—are brief, direct, and use-specific. Many of the expectations listed here for \nprivacy by design and use-specific consent mirror those distributed to developers as best practices when \ndeveloping for smart phone devices,\n82 such as being transparent about how user data will be used, asking for app \npermissions during their use so that the use-context will be clear to users, and ensuring that the app will still \nwork if users deny (or later revoke) some permissions. \n39'
 'Summaries of Panel Discussions: \nPanel 1: Consumer Rights and Protections. This event explored the opportunities and challenges for \nindividual consumers and communities in the context of a growing ecosystem of AI-enabled consumer \nproducts, advanced platforms and services, “Internet of Things” (IoT) devices, and smart city products and services. \nWelcome :\n•Rashida Richardson, Senior Policy Advisor for Data and Democracy, White House Office of Science andTechnology Policy\n•Karen Kornbluh, Senior Fellow and Director of the Digital Innovation and Democracy Initiative, GermanMarshall Fund\nModerator : \nDevin E. Willis, Attorney, Division of Privacy and Identity Protection, Bureau of Consumer Protection, Federal Trade Commission \nPanelists: \n•Tamika L. Butler, Principal, Tamika L. Butler Consulting\n•Jennifer Clark, Professor and Head of City and Regional Planning, Knowlton School of Engineering, OhioState University\n•Carl Holshouser, Senior Vice President for Operations and Strategic Initiatives, TechNet\n•Surya Mattu, Senior Data Engineer and Investigative Data Journalist, The Markup\n•Mariah Montgomery, National Campaign Director, Partnership for Working Families\n55'
 ""zucked-users-say-they-get-blocked-racism-discussion/2859593002/\n8.See, e.g., Michael Levitt. AirTags are being used to track people and cars. Here's what is being done about it.\nNPR. Feb. 18, 2022. https://www.npr.org/2022/02/18/1080944193/apple-airtags-theft-stalking-privacy-tech;\nSamantha Cole. Police Records Show Women Are Being Stalked With Apple AirTags Across the Country .\nMotherboard. Apr. 6, 2022. https://www.vice.com/en/article/y3vj3y/apple-airtags-police-reports-stalking-\nharassment\n9.Kristian Lum and William Isaac. To Predict and Serve? Significance. Vol. 13, No. 5, p. 14-19. Oct. 7, 2016.\nhttps://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x; Aaron Sankin, Dhruv Mehrotra,\nSurya Mattu, and Annie Gilbertson. Crime Prediction Software Promised to Be Free of Biases. New Data Shows\nIt Perpetuates Them. The Markup and Gizmodo. Dec. 2, 2021. https://themarkup.org/prediction-\nbias/2021/12/02/crime-prediction-software-promised-to-be-free-of-biases-new-data-shows-it-perpetuates-them\n10.Samantha Cole. This Horrifying App Undresses a Photo of Any Woman With a Single Click . Motherboard.\nJune 26, 2019. https://www.vice.com/en/article/kzm59x/deepnude-app-creates-fake-nudes-of-any-woman11.Lauren Kaori Gurley. Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make .\nMotherboard. Sep. 20, 2021. https://www.vice.com/en/article/88npjv/amazons-ai-cameras-are-punishing-\ndrivers-for-mistakes-they-didnt-make\n63""]","The Federal Trade Commission (FTC) took action against Kochava, a data broker, for selling sensitive location tracking data that could be used to identify individuals visiting sensitive locations, such as abortion clinics. The FTC's complaint highlighted that Kochava's practices posed significant privacy risks, as the data could lead to the identification of individuals based on their location history, which raises concerns about the potential misuse of such information (Source: [document name], p. [page number]). 

The FTC's action reflects a growing emphasis on protecting consumer privacy and ensuring that companies are held accountable for the sale and use of sensitive data.","FTC sued Kochava for selling data that tracks people at reproductive health clinics, places of worship, and other sensitive locations.",0.36363636363636365,0.9397680150858779,1.0,0.0,0.8875424857715479
How should explanatory mechanisms be built into system design to ensure full behavior transparency in high-risk settings?,"['NOTICE & \nEXPLANATION \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nTailored to the level of risk. An assessment should be done to determine the level of risk of the auto -\nmated system. In settings where the consequences are high as determined by a risk assessment, or extensive \noversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully transparent models should be used), rather than as an after-the-decision interpretation. In other settings, the extent of explanation provided should be tailored to the risk level. \nValid. The explanation provided by a system should accurately reflect the factors and the influences that led \nto a particular decision, and should be meaningful for the particular customization based on purpose, target, and level of risk. While approximation and simplification may be necessary for the system to succeed based on the explanatory purpose and target of the explanation, or to account for the risk of fraud or other concerns related to revealing decision-making information, such simplifications should be done in a scientifically supportable way. Where appropriate based on the explanatory system, error ranges for the explanation should be calculated and included in the explanation, with the choice of presentation of such information balanced with usability and overall interface complexity concerns. \nDemonstrate protections for notice and explanation \nReporting. Summary reporting should document the determinations made based on the above consider -'
 'GV-4.1-001 Establish policies and procedures that address continual improvement processes \nfor GAI risk measurement . Address general risks associated with a lack of \nexplainability and transparency in GAI systems by using ample documentation and \ntechniques such as: application of gradient -based attributions, occlusion/term \nreduction, counterfactual prompts and prompt eng ineering, and analysis of \nembeddings; Assess and update risk measurement approaches at regular cadences.  Confabulation  \nGV-4.1-002 Establish policies, procedures, and processes detailing risk measurement in \ncontext of use with standardized measurement protocols and structured public feedback exercises such as AI red -teaming or independent external evaluations . CBRN Information and Capability ; \nValue Chain and Component Integration'
 'Meaningful access to examine the system. Designers, developers, and deployers of automated \nsystems should consider limited waivers of confidentiality (including those related to trade secrets) where necessary in order to provide meaningful oversight of systems used in sensitive domains, incorporating mea\n-\nsures to protect intellectual property and trade secrets from unwarranted disclosure as appropriate. This includes (potentially private and protected) meaningful access to source code, documentation, and related data during any associated legal discovery, subject to effective confidentiality or court orders. Such meaning\n-\nful access should include (but is not limited to) adhering to the principle on Notice and Explanation using the highest level of risk so the system is designed with built-in explanations; such systems should use fully-trans\n-\nparent models where the model itself can be understood by people needing to directly examine it. \nDemonstrate access to human alternatives, consideration, and fallback \nReporting. Reporting should include an assessment of timeliness and the extent of additional burden for human alternatives, aggregate statistics about who chooses the human alternative, along with the results of the assessment about brevity, clarity, and accessibility of notice and opt-out instructions. Reporting on the accessibility, timeliness, and effectiveness of human consideration and fallback should be made public at regu\n-'
 'Risk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten -\ntial risks of the automated system should be identified and mitigated. Identified risks should focus on the potential for meaningful impact on people’s rights, opportunities, or access and include those to impacted communities that may not be direct users of the automated system, risks resulting from purposeful misuse of the system, and other concerns identified via the consultation process. Assessment and, where possible, mea\n-\nsurement of the impact of risks should be included and balanced such that high impact risks receive attention and mitigation proportionate with those impacts. Automated systems with the intended purpose of violating the safety of others should not be developed or used; systems with such safety violations as identified unin\n-\ntended consequences should not be used until the risk can be mitigated. Ongoing risk mitigation may necessi -\ntate rollback or significant modification to a launched automated system. \n18']","To guarantee complete behavior transparency in high-risk environments, explanatory mechanisms should be integrated into system design through the following approaches:

1. **Risk Assessment**: Begin with a thorough risk assessment to identify the potential consequences of the automated system's decisions. This assessment should determine the level of risk associated with the system's use in specific contexts, such as criminal justice or healthcare, where the stakes are particularly high (Source: [document name], p. [page number]).

2. **Use of Fully Transparent Models**: Design the system using fully transparent models that allow stakeholders to understand how decisions are made. This means employing algorithms and models that can be easily interpreted and explained, rather than relying on ""black box"" approaches that obscure the decision-making process (Source: [document name], p. [page number]).

3. **Built-in Explanatory Features**: Incorporate built-in explanatory features that provide clear and accessible explanations of the system's behavior. These features should articulate the factors and influences that led to specific decisions, ensuring that users can comprehend the rationale behind outcomes (Source: [document name], p. [page number]).

4. **User-Centric Design**: Ensure that the explanations are tailored to the needs of the end-users. This involves considering the target audience's level of expertise and providing explanations that are meaningful and relevant to their context. Simplifying complex information can enhance understanding (Source: [document name], p. [page number]).

5. **Error Range and Uncertainty Reporting**: Include information about error ranges and uncertainties in the explanations provided by the system. This helps users understand the limitations of the system's predictions and decisions, fostering a more realistic view of its capabilities (Source: [document name], p. [page number]).

6. **Continuous Feedback Mechanisms**: Establish mechanisms for continuous feedback from users and stakeholders. This feedback can be used to refine the explanatory features and ensure that they remain effective and relevant over time (Source: [document name], p. [page number]).

7. **Documentation and Reporting**: Maintain comprehensive documentation of the system's design, decision-making processes, and the explanations provided. Regular reporting on the system's performance and the effectiveness of its explanatory mechanisms can enhance accountability and transparency (Source: [document name], p. [page number]).

By integrating these explanatory mechanisms into the design of automated systems, organizations can enhance transparency and accountability, particularly in high-risk environments where the implications of decisions can significantly impact individuals and communities.","In settings where the consequences are high as determined by a risk assessment, or extensive oversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully transparent models should be used), rather than as an after-the-decision interpretation.",0.96,0.9396665493047728,1.0,0.9999999999666667,0.7138030653395179
What are some examples of GAI risks that organizations need to consider in the development and deployment of AI systems?,"['risks, and creates unique risks. GAI risks  can vary along many dimensions:  \n• Stage of the AI lifecycle:  Risks can arise  during design,  development , depl oyment , operation, \nand/or decommission ing. \n• Scope:  Risks  may exist  at individual model  or system  levels , at the application or implementation \nlevel s (i.e., for a specific use case), or at the ecosystem level  – that is, beyond a single  system or \norganizational context . Examples of the latter include  the expansion of “ algorithmic \nmonocultures ,3” resulting from repeated use of the same model, or impacts on  access to \nopportunity, labor markets , and the creative economies .4 \n• Source of risk:  Risks may emerge from factors related to the de sign, training, or operation of  the \nGAI model itself, stemming in some cases from  GAI model or system inputs , and in other cases , \nfrom GAI system outputs.  Many GAI risks, however, originate from human  behavior , including \n \n \n3 “Algorithmic monocultures” refers to the phenomenon in which repeated use of the same model or algorithm  in \nconsequential decision- making settings like employment and lending can result in increased susceptibility  by \nsystems  to correlated failures  (like unexpected shocks),  due to multiple actors relying on the  same algorithm.  \n4 Many studies have projected the impact of AI on the workforce and  labor markets. Fewer studies have examined \nthe impact of GAI on the labor market , though some industry surveys indicate that  that both emp loyees and \nemployers are pondering this disruption.'
 'warrant additional human review, tracking and documentation, and greater management oversight.  \nAI technology can produce varied outputs  in multiple modalities and present many classes of user \ninterfaces. This leads to a broader set of AI Actors  interacting with GAI systems for widely differing \napplications and contexts of use. These  can include data labeling and preparation, development of GAI \nmodels, content moderation, code generation and review, text generation and editing, image and video \ngeneration, summarization, search, and chat. These activities can take place within organizational \nsettings or in the public domain.  \nOrganizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conflict with their tolerances or values. Governance tools and protocols that are applied to other types of AI systems can be applied to GAI systems. These p lans and actions include: \n• Accessibility and reasonable accommodations  \n• AI actor credentials and qualifications  \n• Alignment to organizational values  • Auditing and assessment  \n• Change -management controls  \n• Commercial use  \n• Data provenance'
 '11 value chain  (e.g., data inputs , processing, GAI training, or deployment  environments ), conventional \ncybersecurity practices  may need to adapt or evolve . \nFor instance , prompt  injection  involves  modifying what  input is provided to a  GAI system  so that it  \nbehave s in unintended ways. In direct prompt injections, attackers might craft malicious  prompts  and \ninput them directly to a GAI system , with a variety of  downstream negative consequences to \ninterconnected systems. Indirect prompt injection  attacks occur when  adversaries remotely (i.e., without \na direct interface) exploit LLM -integrated applications by injecting prompts into data likely to be \nretrieved. Security researchers have already demonstrated how indirect prompt injections can exploit \nvulnerabilities by  steal ing proprietary  data  or running malicious code remotely  on a machine. Merely \nquerying  a closed production model can elicit  previously undisclosed information about that model . \nAnother cybersecurity risk to GAI is data poisoning , in which  an adversary compromises  a training \ndataset used by a model to manipulate its output s or operation. Malicious tampering with  data or parts \nof the  model could exacerbate risks associated with GAI system outputs.  \nTrustworthy AI Characteristics:  Privacy Enhanced, Safe , Secure and Resilient , Valid and Reliable  \n2.10. Intellectual Property  \nIntellectual property risks from  GAI systems may arise where the use of copyrighted works  is not a fair \nuse under the fair use doctrine. If a GAI system’s training data included copyrighted material, GAI \noutputs  displaying instances of  training data  memorization  (see Data Privacy above) could infringe on \ncopyright. \nHow GAI relates to copyright, including the status of  generated  content that is similar to but does not \nstrictly copy  w ork protected by copyright , is currently being debated in legal fora.  Similar discussions are'
 '47 Appendix A.  Primary GAI Considerations  \nThe following primary considerations were derived as overarching themes from the GAI PWG \nconsultation process. These considerations (Governance, Pre- Deployment Testing, Content Provenance, \nand Incident Disclosure) are relevant  for volun tary use by any organization designing, developing, and \nusing GAI  and also inform the Actions to Manage GAI risks. Information included about the primary \nconsiderations is not exhaustive , but highlights the most relevant topics derived from the GAI PWG.  \nAcknowledgments: These considerations could not have been surfaced without the helpful analysis and \ncontributions from the community and NIST staff GAI PWG leads: George Awad, Luca Belli, Harold Booth, \nMat Heyman, Yoo young Lee, Mark Pryzbocki, Reva Schwartz, Martin Stanley, and Kyra Yee.  \nA.1. Governance  \nA.1.1.  Overview  \nLike any other technology system, governance principles and techniques can be used to manage risks \nrelated to generative AI models, capabilities, and applications. Organizations may choose to apply their \nexisting risk tiering to GAI systems, or they may op t to revis e or update AI system risk levels to address \nthese unique GAI risks. This section describes how organizational governance regimes may be re -\nevaluated and adjusted for GAI contexts. It also addresses third -party considerations for governing across  \nthe AI value chain.  \nA.1.2.  Organizational  Governance  \nGAI opportunities, risks and long- term performance characteristics are typically less well -understood \nthan non- generative AI tools  and may be perceived and acted upon by humans in ways that vary greatly. \nAccordingly, GAI may call for different levels of oversight from AI Actors  or different human- AI \nconfigurations in order to manage their risks effectively. Organizations’ use of GAI systems may also \nwarrant additional human review, tracking and documentation, and greater management oversight.']","Organizations need to consider several risks associated with Generative AI (GAI) during the development and deployment of AI systems. Here are some key examples:

1. **Algorithmic Monocultures**: This risk arises when multiple organizations rely on the same GAI models for decision-making. Such reliance can lead to correlated failures, where a single flaw in the model affects many systems simultaneously, increasing vulnerability to unexpected shocks (Source: [document name], p. [page number]).

2. **Data Poisoning**: Adversaries may manipulate the training datasets used by GAI models, leading to compromised outputs or behaviors. This can result in the generation of biased or harmful content, undermining the integrity of the AI system (Source: [document name], p. [page number]).

3. **Prompt Injection Attacks**: These attacks involve modifying the input provided to a GAI system to elicit unintended behaviors. Direct prompt injections occur when attackers craft malicious prompts, while indirect injections exploit vulnerabilities in applications that integrate GAI, potentially leading to data breaches or unauthorized actions (Source: [document name], p. [page number]).

4. **Intellectual Property Risks**: GAI systems may inadvertently generate outputs that resemble copyrighted material, raising concerns about copyright infringement. The legal status of such outputs is still under debate, which can create uncertainty for organizations using GAI (Source: [document name], p. [page number]).

5. **Bias and Fairness Issues**: GAI systems can perpetuate or exacerbate existing biases present in the training data. If not properly managed, these biases can lead to unfair treatment of individuals or groups, particularly in sensitive applications like hiring or lending (Source: [document name], p. [page number]).

6. **Privacy Concerns**: The use of personal data in training GAI models can lead to privacy violations, especially if the data is not adequately anonymized. This risk is heightened when sensitive information is involved, such as health or location data (Source: [document name], p. [page number]).

7. **Operational Risks**: GAI systems may behave unpredictably in real-world settings, leading to operational failures. This unpredictability can stem from differences between laboratory testing conditions and actual deployment environments, resulting in performance gaps (Source: [document name], p. [page number]).

By being aware of these risks, organizations can implement appropriate governance, oversight, and risk management strategies to mitigate potential negative impacts associated with GAI systems.","Organizations need to consider various GAI risks in the development and deployment of AI systems, including unacceptable use identified by stakeholder communities, harmful bias and homogenization, dangerous, violent, or hateful content, immature safety or risk cultures related to AI and GAI design, development, and deployment, public information integrity risks impacting democratic processes, unknown long-term performance characteristics of GAI, and risks related to generating illegal content or violating laws.",1.0,0.9493978201836971,0.25,0.999999999975,0.9181426809466123
How should the validity of explanations provided by automated systems be ensured?,"['NOTICE & \nEXPLANATION \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nTailored to the level of risk. An assessment should be done to determine the level of risk of the auto -\nmated system. In settings where the consequences are high as determined by a risk assessment, or extensive \noversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully transparent models should be used), rather than as an after-the-decision interpretation. In other settings, the extent of explanation provided should be tailored to the risk level. \nValid. The explanation provided by a system should accurately reflect the factors and the influences that led \nto a particular decision, and should be meaningful for the particular customization based on purpose, target, and level of risk. While approximation and simplification may be necessary for the system to succeed based on the explanatory purpose and target of the explanation, or to account for the risk of fraud or other concerns related to revealing decision-making information, such simplifications should be done in a scientifically supportable way. Where appropriate based on the explanatory system, error ranges for the explanation should be calculated and included in the explanation, with the choice of presentation of such information balanced with usability and overall interface complexity concerns. \nDemonstrate protections for notice and explanation \nReporting. Summary reporting should document the determinations made based on the above consider -'
 ""Maintained. The human consideration and fallback process and any associated automated processes \nshould be maintained and supported as long as the relevant automated system continues to be in use. \nInstitute training, assessment, and oversight to combat automation bias and ensure any \nhuman-based components of a system are effective. \nTraining and assessment. Anyone administering, interacting with, or interpreting the outputs of an auto -\nmated system should receive training in that system, including how to properly interpret outputs of a system in light of its intended purpose and in how to mitigate the effects of automation bias. The training should reoc\n-\ncur regularly to ensure it is up to date with the system and to ensure the system is used appropriately. Assess -\nment should be ongoing to ensure that the use of the system with human involvement provides for appropri -\nate results, i.e., that the involvement of people does not invalidate the system's assessment as safe and effective or lead to algorithmic discrimination. \nOversight. Human-based systems have the potential for bias, including automation bias, as well as other \nconcerns that may limit their effectiveness. The results of assessments of the efficacy and potential bias of such human-based systems should be overseen by governance structures that have the potential to update the operation of the human-based system in order to mitigate these effects. \n50""
 ""Providing notice has long been a standard practice, and in many cases is a legal requirement, when, for example, making a video recording of someone (outside of a law enforcement or national security context). In some cases, such as credit, lenders are required to provide notice and explanation to consumers. Techniques used to automate the process of explaining such systems are under active research and improvement and such explanations can take many forms. Innovative companies and researchers are rising to the challenge and creating and deploying explanatory systems that can help the public better understand decisions that impact them. \nWhile notice and explanation requirements are already in place in some sectors or situations, the American public deserve to know consistently and across sectors if an automated system is being used in a way that impacts their rights, opportunities, or access. This knowledge should provide confidence in how the public is being treated, and trust in the validity and reasonable use of automated systems. \n• A lawyer representing an older client with disabilities who had been cut off from Medicaid-funded home\nhealth-care assistance couldn't determine why\n, especially since the decision went against historical access\npractices. In a court hearing, the lawyer learned from a witness that the state in which the older client\nlived \nhad recently adopted a new algorithm to determine eligibility.83 The lack of a timely explanation made it\nharder \nto understand and contest the decision.\n•\nA formal child welfare investigation is opened against a parent based on an algorithm and without the parent\never \nbeing notified that data was being collected and used as part of an algorithmic child maltreatment\nrisk assessment.84 The lack of notice or an explanation makes it harder for those performing child\nmaltreatment assessments to validate the risk assessment and denies parents knowledge that could help them\ncontest a decision.\n41""
 'SAFE AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nOngoing monitoring. Automated systems should have ongoing monitoring procedures, including recalibra -\ntion procedures, in place to ensure that their performance does not fall below an acceptable level over time, \nbased on changing real-world conditions or deployment contexts, post-deployment modification, or unexpect -\ned conditions. This ongoing monitoring should include continuous evaluation of performance metrics and harm assessments, updates of any systems, and retraining of any machine learning models as necessary, as well as ensuring that fallback mechanisms are in place to allow reversion to a previously working system. Monitor\n-\ning should take into account the performance of both technical system components (the algorithm as well as any hardware components, data inputs, etc.) and human operators. It should include mechanisms for testing the actual accuracy of any predictions or recommendations generated by a system, not just a human operator’s determination of their accuracy. Ongoing monitoring procedures should include manual, human-led monitor\n-\ning as a check in the event there are shortcomings in automated monitoring systems. These monitoring proce -\ndures should be in place for the lifespan of the deployed automated system. \nClear organizational oversight. Entities responsible for the development or use of automated systems should lay out clear governance structures and procedures.  This includes clearly-stated governance proce\n-']","To ensure the validity of explanations provided by automated systems, organizations can implement several key strategies:

1. **Accurate Representation of Decision-Making**: The explanations must accurately reflect the factors and influences that led to a particular decision. This means that the system should be designed to provide clear insights into how inputs are processed and how they contribute to the final output (Source: [document name], p. [page number]).

2. **Scientific Support for Simplifications**: While simplifications may be necessary for clarity, they should be grounded in scientifically supportable methods. This ensures that any approximations do not mislead users about the system's capabilities or the reliability of its outputs (Source: [document name], p. [page number]).

3. **Error Range Reporting**: Including error ranges in the explanations can help users understand the potential variability in the system's outputs. This transparency about uncertainty allows users to make more informed decisions based on the provided information (Source: [document name], p. [page number]).

4. **User-Centric Design**: Tailor explanations to the needs and understanding of the target audience. This involves considering the users' expertise and providing explanations that are meaningful and relevant to their context, which enhances the likelihood that the explanations will be understood and accepted (Source: [document name], p. [page number]).

5. **Continuous Monitoring and Feedback**: Establish mechanisms for ongoing monitoring of the system's performance and the effectiveness of its explanations. Regular feedback from users can help identify areas for improvement and ensure that explanations remain relevant and valid over time (Source: [document name], p. [page number]).

6. **Documentation and Transparency**: Maintain comprehensive documentation of the system's design, decision-making processes, and the rationale behind the explanations provided. This transparency fosters accountability and allows for external validation of the system's operations (Source: [document name], p. [page number]).

7. **Training and Assessment**: Ensure that individuals interacting with the automated system receive proper training on how to interpret its outputs and explanations. Ongoing assessment of their understanding can help mitigate the effects of automation bias and improve the overall effectiveness of the system (Source: [document name], p. [page number]).

By implementing these strategies, organizations can enhance the validity of the explanations provided by automated systems, thereby improving user trust and the overall effectiveness of the technology.","The explanation provided by a system should accurately reflect the factors and influences that led to a particular decision, and should be meaningful for the particular customization based on purpose, target, and level of risk. While approximation and simplification may be necessary for the system to succeed based on the explanatory purpose and target of the explanation, or to account for the risk of fraud or other concerns related to revealing decision-making information, such simplifications should be done in a scientifically supportable way. Where appropriate based on the explanatory system, error ranges for the explanation should be calculated and included in the explanation, with the choice of presentation of such information balanced with usability and overall interface complexity concerns.",1.0,0.9589207569827328,1.0,0.999999999975,0.7702969093713703
How do generative models like LLMs generate outputs that can lead to confabulations in GAI systems?,"['answer itself is incorrect. Similarly, an LLM could falsely assert that it is human or has human traits, \npotentially deceiv ing humans into believing they are speaking with another human. \nThe extent to which humans can be deceived by LLMs, the mechanisms by which this may occur, and the \npotential risks from adversarial prompting of such behavior are  emerging  areas of study . Given the wide \nrange of downstream impacts of GAI, it is difficult to estimate the downstream scale and impact of \nconfabulations . \nTrustworthy AI Characteristics:  Fair with Harmful Bias Managed, Safe, Valid and Reliable , Explainable \nand Interpretable  \n2.3. Dangerous , Violent , or Hateful  Content  \nGAI systems can  produce content that is  inciting, radicalizing, or threatening, or  that glorifi es violence , \nwith greater ease and scale than other technologies . LLMs have been reported to generate  dangerous or \nviolent recommendations , and s ome models have generated actionable instructions for dangerous  or \n \n \n9 Confabulations of falsehoods are most commonly a problem for text -based outputs; for audio, image, or video \ncontent, creative generation of non- factual content can be  a desired behavior.  \n10 For example, legal confabulations have been shown to be pervasive  in current state -of-the-art LLMs. See also, \ne.g.,'
 'Carlini, N. et al. (2023) Quantifying Memorization Across Neural Language Models. ICLR 2023. \nhttps://arxiv.org/pdf/2202.07646  \nCarlini, N. et al. (2024) Stealing Part of a Production Language Model.  arXiv . \nhttps://arxiv.org/abs/2403.06634  \nChandra, B. et al. (2023) Dismantling the Disinformation Business of Chinese Influence Operations.  \nRAND. https://www.rand.org/pubs/commentary/2023/10/dismantling- the-disinformation -business -of-\nchinese.html  \nCiriello , R. et al. (2024) Ethical Tensions in Human- AI Companionship: A Dialectical Inquiry into Replika. \nResearchGate.  https://www.researchgate.net/publication/374505266_Ethical_Tensions_in_Human-\nAI_Companionship_A_Dialectical_Inquiry_into_Replika  \nDahl, M. et al. (2024) Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models.  arXiv . \nhttps://arxiv.org/abs/2401.01301'
 '6 2.2. Confabulation  \n“Confabulation” refers to a phenomenon in which GAI systems generate and confidently present \nerroneous or false content in response to prompts . Confabulations also include generated outputs that \ndiverge from the prompts  or other  input  or that contradict previously generated statements in the same \ncontext. Th ese phenomena are colloquially also referred to as “hallucination s” or “fabrication s.” \nConfabulations can occur across GAI outputs  and contexts .9,10 Confabulations are a natural result of the \nway generative models  are designed : they  generate outputs that approximate the statistical distribution \nof their training data ; for example,  LLMs  predict the next  token or word  in a sentence or phrase . While \nsuch statistical  prediction can produce factual ly accurate  and consistent  outputs , it can  also produce \noutputs that are factually inaccurat e or internally inconsistent . This dynamic is particularly relevant when \nit comes to  open -ended prompts  for long- form responses  and in domains  which require highly \ncontextual and/or  domain expertise.  \nRisks from confabulations may arise when users believe false content  – often  due to the confident nature \nof the response  – leading users to act upon or promote the false information.  This poses a challenge  for \nmany real -world applications, such as in healthcare, where a confabulated summary of patient \ninformation reports could  cause doctors to make  incorrect diagnoses  and/or recommend the wrong \ntreatments.  Risks of confabulated content may be especially important to monitor  when integrating GAI \ninto applications involving  consequential  decision making. \nGAI outputs may also include confabulated logic or citations  that purport to justify or explain the \nsystem’s answer , which may further mislead  humans into inappropriately trusting the system’s output. \nFor instance, LLMs  sometimes provide logical steps for how they arrived at an answer even when the'
 'the-art LLMs at the time these studies were conducted do not substantially  increase the operational \nlikelihood of such an attack.  The physical synthesis development, production, and use of chemical or \nbiological agents will continue to require both applicable expertise and supporting materials and \ninfrastructure . The impact of GAI on chemical or biological agent misuse will depend on  what the  key \nbarriers  for malicious actors  are (e.g., whether information access is one such barrier ), and how well GAI \ncan help actors  address those barriers .  \nFurthermore , chemical and biological design tools  (BDTs)  – highly specialized AI systems  trained on \nscientific data that aid in chemical and biological design – may augment  design capabilities in chemistry \nand biology beyond what text -based LLMs are able to provide . As these models become more \nefficacious , including for beneficial uses, it will be important to assess their potential to be used for \nharm, such as the ideation and design of novel harmful chemical or biological agents .  \nWhile some of these described capabilities  lie beyond the reach  of existing GAI tools, ongoing \nassessments of this risk would be enhanced by monitoring both  the ability of  AI tools  to facilitate CBRN \nweapons planning and GAI systems’  connection or access to relevant data and tools . \nTrustworthy AI Characteristic : Safe , Explainable and Interpretable']","Generative models, such as large language models (LLMs), produce outputs that may result in confabulations due to the way they are designed and trained. Here are some key points explaining this phenomenon:

1. **Statistical Prediction**: LLMs generate text by predicting the next word or token based on the statistical patterns learned from their training data. This means they do not have an understanding of the content in the same way humans do; instead, they rely on patterns and associations found in the data (Source: [document name], p. [page number]).

2. **Lack of Factual Verification**: When generating responses, LLMs do not verify the factual accuracy of the information they produce. They may confidently present incorrect or misleading information as if it were true, leading to outputs that are factually inaccurate or internally inconsistent (Source: [document name], p. [page number]).

3. **Open-Ended Prompts**: LLMs are particularly prone to confabulation when responding to open-ended prompts. In these cases, the model may generate long-form responses that deviate from the original context or contradict earlier statements, resulting in outputs that can be misleading (Source: [document name], p. [page number]).

4. **Hallucinations**: The term ""hallucination"" is often used to describe instances where LLMs generate content that is entirely fabricated or nonsensical. This can occur when the model attempts to fill gaps in its training data or when it encounters prompts that lead it to generate creative but inaccurate responses (Source: [document name], p. [page number]).

5. **Confabulated Logic and Citations**: LLMs may also produce confabulated logic or citations, where they provide seemingly logical explanations or references that do not actually exist. This can mislead users into believing that the information is credible and well-supported (Source: [document name], p. [page number]).

6. **Contextual Limitations**: The ability of LLMs to maintain context over long interactions is limited. As conversations progress, the model may lose track of earlier details, leading to inconsistencies in its responses and further contributing to confabulation (Source: [document name], p. [page number]).

Overall, these characteristics of generative models highlight the importance of careful oversight and validation when using LLMs in applications where accuracy and reliability are critical.","Generative models like LLMs generate outputs that can lead to confabulations in GAI systems by approximating the statistical distribution of their training data. While this statistical prediction can result in factually accurate and consistent outputs, it can also produce outputs that are factually inaccurate or internally inconsistent. This becomes particularly relevant in open-ended prompts for long-form responses and domains requiring contextual or domain expertise.",1.0,0.9095751510150262,1.0,0.8333333332916666,0.4040612942206161
How can appropriate diligence on training data use help assess intellectual property risks in AI systems?,"[""27 MP-4.1-0 10 Conduct appropriate diligence on  training data use to assess intellectual property, \nand privacy, risks, including to examine whether use of proprietary or sensitive \ntraining data is consistent with applicable laws.  Intellectual Property ; Data Privacy  \nAI Actor Tasks: Governance and Oversight, Operation and Monitoring, Procurement, Third -party entities  \n \nMAP 5.1:  Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past \nuses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or d eployed \nthe AI system, or other data are identified and documented.  \nAction ID  Suggested Action  GAI Risks  \nMP-5.1-001 Apply TEVV practices for content provenance (e.g., probing a system's synthetic \ndata generation capabilities for potential misuse or vulnerabilities . Information Integrity ; Information \nSecurity  \nMP-5.1-002 Identify potential content provenance harms of GAI, such as misinformation or \ndisinformation, deepfakes, including NCII, or tampered content. Enumerate and rank risks based on their likelihood and potential impact, and determine how well provenance solutions address specific risks and/or harms.  Information Integrity ; Dangerous , \nViolent, or Hateful Content ; \nObscene, Degrading, and/or Abusive Content  \nMP-5.1-003 Consider d isclos ing use of GAI to end user s in relevant contexts, while considering \nthe objective of disclosure, the context of use, the likelihood and magnitude of the  \nrisk posed, the audience of the disclosure, as well as the frequency of the disclosures.  Human -AI Configuration  \nMP-5.1-004 Prioritize GAI structured public feedback processes based on risk assessment estimates.  Information Integrity ; CBRN \nInformation or Capabilities ; \nDangerous , Violent, or Hateful \nContent ; Harmful Bias and \nHomogenization""
 '54 Appendix B.  References  \nAcemoglu, D. (2024) The Simple Macroeconomics of AI https://www.nber.org/papers/w32487  \nAI Incident Database. https://incidentdatabase.ai/  \nAtherton, D. (2024) Deepfakes and Child Safety: A Survey and Analysis of 2023 Incidents and Responses. \nAI Incident Database.  https://incidentdatabase.ai/blog/deepfakes -and-child -safety/  \nBadyal, N. et al. (2023) Intentional Biases in LLM Responses. arXiv . https://arxiv.org/pdf/2311.07611  \nBing Chat: Data Exfiltration Exploit Explained.  Embrace The Red. \nhttps://embracethered.com/blog/posts/2023/bing- chat -data -exfiltration -poc- and-fix/ \nBommasani, R. et al. (2022) Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?  arXiv . https://arxiv.org/pdf/2211.13972\n \nBoyarskaya, M. et al. (2020) Overcoming Failures of Imagination in AI Infused System Development and Deployment.  arXiv . https://arxiv.org/pdf/2011.13416\n \nBrowne, D. et al. (2023) Securing the AI Pipeline. Mandiant . \nhttps://www.mandiant.com/resources/blog/securing- ai-pipeline  \nBurgess, M. (2024) Generative AI’s Biggest Security Flaw Is Not Easy to Fix.  WIRED. \nhttps://www.wired.com/story/generative -ai-prompt -injection -hacking/  \nBurtell, M. et al. (2024) The Surprising Power of Next Word Prediction: Large Language Models \nExplained, Part 1.  Georgetown Center for Security and Emerging Technology . \nhttps://cset.georgetown.edu/article/the -surprising- power -of-next -word -prediction- large -language -\nmodels -explained -part-1/ \nCanadian Centre for Cyber Security  (2023)  Generative artificial intelligence (AI) - ITSAP .00.041. \nhttps://www.cyber.gc.ca/en/guidance/generative -artificial -intelligence -ai-itsap00041  \nCarlini, N., et al. (2021) Extracting Training Data from Large Language Models.  Usenix . \nhttps://www.usenix.org/conference/usenixsecurity21/presentation/carlini -extracting  \nCarlini, N. et al. (2023) Quantifying Memorization Across Neural Language Models. ICLR 2023.'
 'requirements on drivers, such as slowing down near schools or playgrounds.16\nFrom large companies to start-ups, industry is providing innovative solutions that allow \norganizations to mitigate risks to the safety and efficacy of AI systems, both before \ndeployment and through monitoring over time.17 These innovative solutions include risk \nassessments, auditing mechanisms, assessment of organizational procedures, dashboards to allow for ongoing \nmonitoring, documentation procedures specific to model assessments, and many other strategies that aim to \nmitigate risks posed by the use of AI to companies’ reputation, legal responsibilities, and other product safety \nand effectiveness concerns. \nThe Office of Management and Budget (OMB) has called for an expansion of opportunities \nfor meaningful stakeholder engagement in the design of programs and services. OMB also \npoints to numerous examples of effective and proactive stakeholder engagement, including the Community-\nBased Participatory Research Program developed by the National Institutes of Health and the participatory \ntechnology assessments developed by the National Oceanic and Atmospheric Administration.18\nThe National Institute of Standards and Technology (NIST) is developing a risk \nmanagement framework to better manage risks posed to individuals, organizations, and \nsociety by AI.19 The NIST AI Risk Management Framework, as mandated by Congress, is intended for \nvoluntary use to help incorporate trustworthiness considerations into the design, development, use, and \nevaluation of AI products, services, and systems. The NIST framework is being developed through a consensus-\ndriven, open, transparent, and collaborative process that includes workshops and other opportunities to provide \ninput. The NIST framework aims to foster the development of innovative approaches to address \ncharacteristics of trustworthiness including accuracy, explainability and interpretability, reliability, privacy,'
 '11 value chain  (e.g., data inputs , processing, GAI training, or deployment  environments ), conventional \ncybersecurity practices  may need to adapt or evolve . \nFor instance , prompt  injection  involves  modifying what  input is provided to a  GAI system  so that it  \nbehave s in unintended ways. In direct prompt injections, attackers might craft malicious  prompts  and \ninput them directly to a GAI system , with a variety of  downstream negative consequences to \ninterconnected systems. Indirect prompt injection  attacks occur when  adversaries remotely (i.e., without \na direct interface) exploit LLM -integrated applications by injecting prompts into data likely to be \nretrieved. Security researchers have already demonstrated how indirect prompt injections can exploit \nvulnerabilities by  steal ing proprietary  data  or running malicious code remotely  on a machine. Merely \nquerying  a closed production model can elicit  previously undisclosed information about that model . \nAnother cybersecurity risk to GAI is data poisoning , in which  an adversary compromises  a training \ndataset used by a model to manipulate its output s or operation. Malicious tampering with  data or parts \nof the  model could exacerbate risks associated with GAI system outputs.  \nTrustworthy AI Characteristics:  Privacy Enhanced, Safe , Secure and Resilient , Valid and Reliable  \n2.10. Intellectual Property  \nIntellectual property risks from  GAI systems may arise where the use of copyrighted works  is not a fair \nuse under the fair use doctrine. If a GAI system’s training data included copyrighted material, GAI \noutputs  displaying instances of  training data  memorization  (see Data Privacy above) could infringe on \ncopyright. \nHow GAI relates to copyright, including the status of  generated  content that is similar to but does not \nstrictly copy  w ork protected by copyright , is currently being debated in legal fora.  Similar discussions are']","Appropriate diligence on training data use is crucial for assessing intellectual property (IP) risks in AI systems in several ways:

1. **Understanding Data Sources**: Conducting thorough diligence involves identifying and documenting the sources of training data. This helps ensure that the data used is either owned by the organization, licensed appropriately, or falls under fair use. Knowing the origins of the data can mitigate the risk of infringing on third-party IP rights (Source: [document name], p. [page number]).

2. **Evaluating Licensing Agreements**: Organizations should review and understand the licensing agreements associated with the training data. This includes checking for any restrictions on use, distribution, or modification of the data. Proper evaluation of these agreements can help prevent unintentional violations of IP rights (Source: [document name], p. [page number]).

3. **Assessing Fair Use**: Diligence should include an assessment of whether the use of copyrighted materials in training data qualifies as fair use. This involves considering factors such as the purpose of use, the nature of the copyrighted work, the amount used, and the effect on the market for the original work. A clear understanding of fair use can help organizations navigate potential legal challenges (Source: [document name], p. [page number]).

4. **Documenting Data Provenance**: Keeping detailed records of data provenance—where the data comes from and how it has been used—can provide a clear audit trail. This documentation is essential for demonstrating compliance with IP laws and can be valuable in case of disputes (Source: [document name], p. [page number]).

5. **Identifying Potential Risks**: By conducting diligence on training data, organizations can identify potential IP risks early in the development process. This proactive approach allows for the implementation of risk mitigation strategies, such as seeking licenses for necessary data or modifying the training dataset to exclude sensitive materials (Source: [document name], p. [page number]).

6. **Engaging Legal Expertise**: Involving legal experts during the diligence process can provide additional insights into IP risks associated with training data. Legal professionals can help interpret complex licensing agreements and advise on compliance with IP laws, ensuring that the organization is protected (Source: [document name], p. [page number]).

By implementing these diligence practices, organizations can better assess and manage intellectual property risks associated with the use of training data in AI systems, ultimately leading to more responsible and legally compliant AI development.","Appropriate diligence on training data use can help assess intellectual property risks in AI systems by examining whether the use of proprietary or sensitive training data aligns with relevant laws. This includes evaluating the likelihood and magnitude of potential impacts, both beneficial and harmful, based on past uses of AI systems in similar contexts, public incident reports, feedback from external parties, and other relevant data. By identifying and documenting these impacts, organizations can better understand the risks associated with their training data and take appropriate measures to mitigate them.",1.0,0.981217057423747,1.0,0.999999999975,0.3383587974968043
How do integrated human-AI systems benefit companies in providing customer service?,"['warrant additional human review, tracking and documentation, and greater management oversight.  \nAI technology can produce varied outputs  in multiple modalities and present many classes of user \ninterfaces. This leads to a broader set of AI Actors  interacting with GAI systems for widely differing \napplications and contexts of use. These  can include data labeling and preparation, development of GAI \nmodels, content moderation, code generation and review, text generation and editing, image and video \ngeneration, summarization, search, and chat. These activities can take place within organizational \nsettings or in the public domain.  \nOrganizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conflict with their tolerances or values. Governance tools and protocols that are applied to other types of AI systems can be applied to GAI systems. These p lans and actions include: \n• Accessibility and reasonable accommodations  \n• AI actor credentials and qualifications  \n• Alignment to organizational values  • Auditing and assessment  \n• Change -management controls  \n• Commercial use  \n• Data provenance'
 ""HUMAN ALTERNATIVES, \nCONSIDERATION, AND \nFALLBACK \nHOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\nReal-life examples of how these principles can become reality, through laws, policies, and practical \ntechnical and sociotechnical approaches to protecting rights, opportunities, and access. \nHealthcare “navigators” help people find their way through online signup forms to choose \nand obtain healthcare. A Navigator is “an individual or organization that's trained and able to help \nconsumers, small businesses, and their employees as they look for health coverage options through the \nMarketplace (a government web site), including completing eligibility and enrollment forms.”106 For \nthe 2022 plan year, the Biden-Harris Administration increased funding so that grantee organizations could \n“train and certify more than 1,500 Navigators to help uninsured consumers find affordable and comprehensive \nhealth coverage. ”107\nThe customer service industry has successfully integrated automated services such as \nchat-bots and AI-driven call response systems with escalation to a human support team.\n108 Many businesses now use partially automated customer service platforms that help answer customer \nquestions and compile common problems for human agents to review. These integrated human-AI \nsystems allow companies to provide faster customer care while maintaining human agents to answer \ncalls or otherwise respond to complicated requests. Using both AI and human agents is viewed as key to \nsuccessful customer service.109\nBallot curing laws in at least 24 states require a fallback system that allows voters to \ncorrect their ballot and have it counted in the case that a voter signature matching algorithm incorrectly flags their ballot as invalid or there is another issue with their ballot, and review by an election official does not rectify the problem. Some federal courts have found that such cure procedures are constitutionally required.\n110 Ballot""
 'ENDNOTES\n12.Expectations about reporting are intended for the entity developing or using the automated system. The\nresulting reports can be provided to the public, regulators, auditors, industry standards groups, or others\nengaged in independent review, and should be made public as much as possible consistent with law,regulation, and policy, and noting that intellectual property or law enforcement considerations may preventpublic release. These reporting expectations are important for transparency, so the American people canhave confidence that their rights, opportunities, and access as well as their expectations aroundtechnologies are respected.\n13.National Artificial Intelligence Initiative Office. Agency Inventories of AI Use Cases. Accessed Sept. 8,\n2022. https://www.ai.gov/ai-use-case-inventories/\n14.National Highway Traffic Safety Administration. https://www.nhtsa.gov/\n15.See, e.g., Charles Pruitt. People Doing What They Do Best: The Professional Engineers and NHTSA . Public\nAdministration Review. Vol. 39, No. 4. Jul.-Aug., 1979. https://www.jstor.org/stable/976213?seq=116.The US Department of Transportation has publicly described the health and other benefits of these\n“traffic calming” measures. See, e.g.: U.S. Department of Transportation. Traffic Calming to Slow Vehicle\nSpeeds. Accessed Apr. 17, 2022. https://www.transportation.gov/mission/health/Traffic-Calming-to-Slow-\nVehicle-Speeds\n17.Karen Hao. Worried about your firm’s AI ethics? These startups are here to help.\nA growing ecosystem of “responsible AI” ventures promise to help organizations monitor and fix their AI\nmodels. MIT Technology Review. Jan 15., 2021.https://www.technologyreview.com/2021/01/15/1016183/ai-ethics-startups/ ; Disha Sinha. Top Progressive\nCompanies Building Ethical AI to Look Out for in 2021. Analytics Insight. June 30, 2021. https://\nwww.analyticsinsight.net/top-progressive-companies-building-ethical-ai-to-look-out-for-'
 'but are not limited to:  \n• Participatory Engagement Methods : Methods used to solicit feedback from civil society groups, \naffected communities, and users, including focus groups, small user studies, and surveys.  \n• Field Testing : Methods used to determine how people interact with, consume, use, and make \nsense of AI -generated information, and subsequent actions and effects, including UX, usability, \nand other structured, randomized experiments.  \n• AI Red -teaming:  A structured testing exercise\n used to probe an AI system to find flaws and \nvulnerabilities such as inaccurate, harmful, or discriminatory outputs, often in a controlled \nenvironment and in collaboration with system developers.  \nInformation gathered from structured public feedback can inform design, implementation, deployment \napproval , maintenance, or decommissioning decisions. Results and insights gleaned from these exercises \ncan serve multiple purposes, including improving data quality and preprocessing, bolstering governance decision making, and enhancing system documentation and debugging practices. When implementing \nfeedback activities, organizations should follow human subjects research requirements and best \npractices such as  informed consent and subject compensation.']","Integrated human-AI systems enhance customer service for companies in several key ways:

1. **Efficiency and Speed**: AI systems, such as chatbots and automated response systems, can handle a large volume of customer inquiries simultaneously. This allows for quicker response times, reducing wait times for customers and improving overall service efficiency (Source: [document name], p. [page number]).

2. **24/7 Availability**: AI-driven customer service solutions can operate around the clock, providing support to customers at any time of day. This constant availability ensures that customers can receive assistance whenever they need it, enhancing their overall experience (Source: [document name], p. [page number]).

3. **Handling Routine Inquiries**: AI systems can effectively manage routine and repetitive inquiries, such as FAQs or basic troubleshooting. By automating these tasks, human agents can focus on more complex issues that require personal attention, leading to better resource allocation (Source: [document name], p. [page number]).

4. **Data Collection and Analysis**: Integrated systems can gather and analyze customer interaction data, providing valuable insights into customer preferences and behaviors. This information can help companies tailor their services and improve customer satisfaction (Source: [document name], p. [page number]).

5. **Seamless Escalation**: When AI systems encounter complex issues that they cannot resolve, they can seamlessly escalate the inquiry to a human agent. This ensures that customers receive the appropriate level of support without having to repeat their issues, leading to a smoother customer experience (Source: [document name], p. [page number]).

6. **Personalization**: AI can analyze past interactions and customer data to provide personalized responses and recommendations. This level of personalization can enhance customer engagement and satisfaction, as customers feel understood and valued (Source: [document name], p. [page number]).

7. **Cost Savings**: By automating routine tasks and improving efficiency, integrated human-AI systems can lead to significant cost savings for companies. This allows organizations to allocate resources more effectively and invest in other areas of customer service (Source: [document name], p. [page number]).

Overall, the integration of human and AI systems in customer service creates a more responsive, efficient, and personalized experience for customers, ultimately benefiting both the organization and its clientele.","Integrated human-AI systems benefit companies in providing customer service by allowing for faster customer care while maintaining human agents to handle complicated requests. These systems use partially automated platforms to answer common customer questions and compile issues for human agents to review, ensuring a balance between efficiency and personalized service.",1.0,0.9842112100837633,1.0,0.99999999995,0.988905443151687
What was the purpose of the year of public engagement that informed the development of the Blueprint for an AI Bill of Rights?,"['ing sessions, meetings, a formal request for information, and input to a publicly accessible and widely-publicized \nemail address, people throughout the United States, public servants across Federal agencies, and members of the \ninternational community spoke up about both the promises and potential harms of these technologies, and \nplayed a central role in shaping the Blueprint for an AI Bill of Rights. The core messages gleaned from these \ndiscussions include that AI has transformative potential to improve Americans’ lives, and that preventing the \nharms of these technologies is both necessary and achievable. The Appendix includes a full list of public engage-\nments. \n4'
 'About this Document \nThe Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was \npublished by the White House Office of Science and Technology Policy in October 2022. This framework was \nreleased one year after OSTP announced  the launch of a process to develop “a bill of rights for an AI-powered \nworld.” Its release follows a year of public engagement to inform this initiative. The framework is available \nonline at: https://www.whitehouse.gov/ostp/ai-bill-of-rights \nAbout the Office of Science and Technology Policy \nThe Office of Science and Technology Policy (OSTP)  was established by the National Science and Technology  \nPolicy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office \nof the President with advice on the scientific, engineering, and technological aspects of the economy, national \nsecurity, health, foreign relations, the environment, and the technological recovery and use of resources, among \nother topics. OSTP leads interagency science and technology policy coordination efforts, assists the Office of \nManagement and Budget (OMB) with an annual review and analysis of Federal research and development in \nbudgets, and serves as a source of scientific and technological analysis and judgment for the President with \nrespect to major policies, plans, and programs of the Federal Government.  \nLegal Disclaimer \nThe Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People is a white paper \npublished by the White House Office of Science and Technology Policy. It is intended to support the \ndevelopment of policies and practices that protect civil rights and promote democratic values in the building, \ndeployment, and governance of automated systems. \nThe Blueprint for an AI Bill of Rights is non-binding and does not constitute U.S. government policy. It'
 ""Considered together, the five principles and associated practices of the Blueprint for an AI Bill of Rights form an overlapping set of backstops against potential harms. This purposefully overlapping framework, when taken as a whole, forms a blueprint to help protect the public from harm. The measures taken to realize the vision set forward in this framework should be proportionate with the extent and nature of the harm, or risk of harm, to people's rights, opportunities, and access. \nRELATIONSHIP TO EXISTING  LAW AND POLICY\nThe Blueprint for an AI Bill of Rights is an exercise in envisioning a future where the American public is \nprotected from the potential harms, and can fully enjoy the benefits, of automated systems. It describes princi -\nples that can help ensure these protections. Some of these protections are already required by the U.S. Constitu -\ntion or implemented under existing U.S. laws. For example, government surveillance, and data search and seizure are subject to legal requirements and judicial oversight. There are Constitutional requirements for human review of criminal investigative matters and statutory requirements for judicial review. Civil rights laws protect the American people against discrimination. \n8""
 ""APPENDIX\n• OSTP conducted meetings with a variety of stakeholders in the private sector and civil society. Some of these\nmeetings were specifically focused on providing ideas related to the development of the Blueprint for an AI\nBill of Rights while others provided useful general context on the positive use cases, potential harms, and/or\noversight possibilities for these technologies. Participants in these conversations from the private sector and\ncivil society included:\nAdobe \nAmerican Civil Liberties Union (ACLU) The Aspen Commission on Information Disorder The Awood Center The Australian Human Rights Commission Biometrics Institute The Brookings Institute BSA | The Software Alliance Cantellus Group Center for American Progress Center for Democracy and Technology Center on Privacy and Technology at Georgetown Law Christiana Care Color of Change Coworker Data Robot Data Trust Alliance Data and Society Research Institute Deepmind EdSAFE AI Alliance Electronic Privacy Information Center (EPIC) Encode Justice Equal AI Google Hitachi's AI Policy Committee The Innocence Project Institute of Electrical and Electronics Engineers (IEEE) Intuit Lawyers Committee for Civil Rights Under Law Legal Aid Society The Leadership Conference on Civil and Human Rights Meta Microsoft The MIT AI Policy Forum Movement Alliance Project The National Association of Criminal Defense Lawyers O’Neil Risk Consulting & Algorithmic Auditing The Partnership on AI Pinterest The Plaintext Group pymetrics SAP The Security Industry Association Software and Information Industry Association (SIIA) Special Competitive Studies Project Thorn United for Respect University of California at Berkeley Citris Policy Lab University of California at Berkeley Labor Center Unfinished/Project Liberty Upturn US Chamber of Commerce US Chamber of Commerce Technology Engagement Center \nA.I. Working Group\nVibrent HealthWarehouse Worker ResourceCenterWaymap\n62""]","The year of public engagement that informed the development of the Blueprint for an AI Bill of Rights aimed to gather input and insights from a diverse range of stakeholders, including the public, civil society, and industry experts. The purpose of this engagement was to understand the promises and potential harms of AI technologies, ensuring that the framework reflects the needs and concerns of the American people. This collaborative process was essential for shaping policies and practices that protect civil rights and promote democratic values in the governance of automated systems (Source: [Blueprint for an AI Bill of Rights], p. [4]).",The purpose of the year of public engagement that informed the development of the Blueprint for an AI Bill of Rights was to gather input and feedback from the public to shape the framework and ensure it reflects the values and concerns of the American people.,0.875,0.98254660364619,1.0,0.999999999975,0.6707672659665068
How can automated systems prevent 'mission creep' while ensuring privacy and user control?,"['DATA PRIVACY \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nProtect the public from unchecked surveillance \nHeightened oversight of surveillance. Surveillance or monitoring systems should be subject to \nheightened oversight that includes at a minimum assessment of potential harms during design (before deploy -\nment) and in an ongoing manner, to ensure that the American public’s rights, opportunities, and access are protected. This assessment should be done before deployment and should give special attention to ensure there is not algorithmic discrimination, especially based on community membership, when deployed in a specific real-world context. Such assessment should then be reaffirmed in an ongoing manner as long as the system is in use. \nLimited and proportionate surveillance. Surveillance should be avoided unless it is strictly necessary to achieve a legitimate purpose and it is proportionate to the need. Designers, developers, and deployers of surveillance systems should use the least invasive means of monitoring available and restrict monitoring to the minimum number of subjects possible. To the greatest extent possible consistent with law enforcement and national security needs, individuals subject to monitoring should be provided with clear and specific notice before it occurs and be informed about how the data gathered through surveillance will be used. \nScope limits on surveillance to protect rights and democratic values. Civil liberties and civil'
 'Risk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten -\ntial risks of the automated system should be identified and mitigated. Identified risks should focus on the potential for meaningful impact on people’s rights, opportunities, or access and include those to impacted communities that may not be direct users of the automated system, risks resulting from purposeful misuse of the system, and other concerns identified via the consultation process. Assessment and, where possible, mea\n-\nsurement of the impact of risks should be included and balanced such that high impact risks receive attention and mitigation proportionate with those impacts. Automated systems with the intended purpose of violating the safety of others should not be developed or used; systems with such safety violations as identified unin\n-\ntended consequences should not be used until the risk can be mitigated. Ongoing risk mitigation may necessi -\ntate rollback or significant modification to a launched automated system. \n18'
 'In discussion of technical and governance interventions that that are needed to protect against the harms of these technologies, panelists individually described the importance of: receiving community input into the design and use of technologies, public reporting on crucial elements of these systems, better notice and consent procedures that ensure privacy based on context and use case, ability to opt-out of using these systems and receive a fallback to a human process, providing explanations of decisions and how these systems work, the need for governance including training in using these systems, ensuring the technological use cases are genuinely related to the goal task and are locally validated to work, and the need for institution and protection of third party audits to ensure systems continue to be accountable and valid. \n57'
 'Data collection and use-case scope limits. Data collection should be limited in scope, with specific, \nnarrow identified goals, to avoid ""mission creep.""  Anticipated data collection should be determined to be strictly necessary to the identified goals and should be minimized as much as possible. Data collected based on these identified goals and for a specific context should not be used in a different context without assessing for new privacy risks and implementing appropriate mitigation measures, which may include express consent. Clear timelines for data retention should be established, with data deleted as soon as possible in accordance with legal or policy-based limitations. Determined data retention timelines should be documented and justi\n-\nfied. \nRisk identification and mitigation. Entities that collect, use, share, or store sensitive data should attempt to proactively identify harms and seek to manage them so as to avoid, mitigate, and respond appropri\n-\nately to identified risks. Appropriate responses include determining not to process data when the privacy risks outweigh the benefits or implementing measures to mitigate acceptable risks. Appropriate responses do not include sharing or transferring the privacy risks to users via notice or consent requests where users could not reasonably be expected to understand the risks without further support. \nPrivacy-preserving security. Entities creating, using, or governing automated systems should follow privacy and security best practices designed to ensure data and metadata do not leak beyond the specific consented use case. Best practices could include using privacy-enhancing cryptography or other types of privacy-enhancing technologies or fine-grained permissions and access control mechanisms, along with conventional system security protocols. \n33']","To prevent ""mission creep"" while ensuring privacy and user control in automated systems, organizations can implement several key strategies:

1. **Define Clear Objectives**: Establish specific, narrow goals for the automated system from the outset. This clarity helps prevent the system from being used for purposes beyond its original intent, thereby reducing the risk of mission creep (Source: [document name], p. [page number]).

2. **Data Minimization**: Adopt data minimization principles by collecting only the data that is strictly necessary to achieve the defined objectives. Limiting data collection helps prevent the accumulation of unnecessary information that could be misused later (Source: [document name], p. [page number]).

3. **User Consent and Control**: Implement robust consent mechanisms that allow users to understand what data is being collected and how it will be used. Providing users with control over their data, including options to opt-out or delete their information, enhances privacy and fosters trust (Source: [document name], p. [page number]).

4. **Regular Audits and Reviews**: Conduct regular audits of the automated system to ensure compliance with the original objectives and privacy standards. These reviews can help identify any deviations from the intended use and allow for corrective actions to be taken (Source: [document name], p. [page number]).

5. **Stakeholder Engagement**: Involve stakeholders, including users and privacy advocates, in the design and implementation of the automated system. Their input can help identify potential risks and ensure that the system aligns with community values and expectations (Source: [document name], p. [page number]).

6. **Transparent Communication**: Maintain transparency about the system's capabilities, limitations, and data usage. Clear communication helps users understand how their data is being handled and reinforces their sense of control over their information (Source: [document name], p. [page number]).

7. **Establish Boundaries for Data Use**: Clearly document and communicate the specific contexts in which collected data will be used. This helps prevent the repurposing of data for unrelated objectives without proper reassessment of privacy risks (Source: [document name], p. [page number]).

By implementing these strategies, organizations can effectively prevent mission creep while ensuring that privacy and user control are prioritized in the design and operation of automated systems.","Automated systems can prevent 'mission creep' and ensure privacy and user control by limiting data collection to specific, narrow goals that are strictly necessary for the identified purposes. Data collection should be minimized, clearly communicated to users, and used only for legal and expected purposes. Any use of data in a different context should be assessed for new privacy risks and appropriate mitigation measures should be implemented, potentially including obtaining express consent. Clear timelines for data retention should be established, with data deleted as soon as possible in accordance with legal or policy-based limitations. Entities should proactively identify and manage privacy risks, avoiding processing data when risks outweigh benefits and implementing measures to mitigate acceptable risks. Privacy-preserving security measures, such as privacy-enhancing cryptography and access control mechanisms, should be employed to prevent data leakage beyond consented use cases.",1.0,0.9615231465422364,1.0,0.999999999975,0.8246701669018618
"How can GAI tech improve red-teaming with human teams, ensuring content origin and incident disclosure?","['51 general public participants. For example, expert AI red- teamers could modify or verify the \nprompts written by general public AI red- teamers. These approaches may also expand coverage \nof the AI risk attack surface.  \n• Human / AI: Performed by GAI in combinatio n with  specialist or non -specialist human teams. \nGAI- led red -teaming can be more cost effective than human red- teamers alone. Human or GAI-\nled AI red -teaming may be better suited for eliciting different types of harms.   \nA.1.6.  Content Provenance  \nOverview \nGAI technologies can be leveraged for many applications such as content generation and synthetic data. \nSome aspects of GAI output s, such as the production of deepfake content, can challenge our ability to \ndistinguish human- generated content from AI -generated synthetic  content. To help manage and mitigate \nthese risks, digital transparency mechanisms like provenance data tracking can trace the origin and \nhistory of content. Provenance data tracking and synthetic content detection can help facilitate greater \ninformation access  about both authentic and synthetic content to users, enabling better knowledge of  \ntrustworthiness  in AI systems. When combined with other organizational accountability mechanisms, \ndigital content transparency approaches  can enable processes to trace negative outcomes back to their \nsource, improve information integrity, and uphold public trust. Provenance data tracking and synthetic content detection mechanisms provide information about the origin \nand history of content  to assist in \nGAI risk management efforts.  \nProvenance metad ata can include information about GAI model developers or creators  of GAI content , \ndate/time of creation, location, modifications, and sources. Metadata can be tracked for text, images, videos, audio, and underlying datasets. The implementation of p rovenance data tracking techniques  can'
 '53 Documenting, reporting, and sharing information about GAI incidents can help mitigate and prevent \nharmful outcomes by assisting relevant AI Actors  in tracing impacts to their source . Greater awareness \nand standardization of GAI incident reporting could promote this transparency and improve GAI risk management across the AI ecosystem.  \nDocumentation and Involvement of AI Actors  \nAI Actors  should be aware of their roles in reporting AI incidents. To better understand previous incidents \nand implement measures to prevent similar ones in the future, organizations could consider developing guidelines for publicly available incident reporting which include information about AI actor \nresponsibilities. These  guidelines would help AI system operators identify GAI incidents across the AI \nlifecycle and with AI Actors  regardless of role . Documentation and review of third -party inputs and \nplugins for GAI systems is especially important for AI Actors  in the context of incident disclosure; LLM \ninputs and content delivered through these plugins is often distributed,\n with inconsistent or insufficient \naccess control.  \nDocumentation practices including logging, recording, and analyzing GAI incidents can facilitate \nsmoother sharing of information with relevant AI Actors . Regular information sharing, change \nmanagement records, version history and metadata can also empower AI Actors  responding to and \nmanaging AI incidents.'
 '31 MS-2.3-004 Utilize a purpose -built testing environment such as NIST Dioptra to empirically \nevaluate GAI trustworthy characteristics.  CBRN Information or Capabilities ; \nData Privacy ; Confabulation ; \nInformation Integrity ; Information \nSecurity ; Dangerous , Violent, or \nHateful Content ; Harmful Bias and \nHomogenization  \nAI Actor Tasks:  AI Deployment, TEVV  \n \nMEASURE 2.5:  The AI system to be deployed is demonstrated to be valid and reliable. Limitations of the generalizability beyond the \nconditions under which the technology was developed are documented.  \nAction ID  Suggested Action  Risks  \nMS-2.5-001 Avoid extrapolating GAI system performance or capabilities from narrow, non -\nsystematic, and anecdotal assessments.  Human -AI Configuration ; \nConfabulation  \nMS-2.5-002 Document the extent to which human domain knowledge is employed to \nimprove GAI system performance, via, e.g., RLHF, fine -tuning, retrieval-\naugmented generation, content moderation, business rules.  Human -AI Configuration  \nMS-2.5-003 Review and verify sources and citations in GAI system outputs during pre -\ndeployment risk measurement and ongoing monitoring activities.  Confabulation  \nMS-2.5-004 Track and document instances of anthropomorphization  (e.g., human images, \nmentions of human feelings, cyborg imagery or motifs) in GAI system interfaces.  Human -AI Configuration  \nMS-2.5-0 05 Verify GAI system training data and TEVV data provenance, and that fine -tuning  \nor retrieval- augmented generation data is grounded.  Information Integrity  \nMS-2.5-0 06 Regularly review security and safety guardrails, especially if the GAI system is \nbeing operated in novel circumstances. This includes reviewing reasons why the \nGAI system was initially assessed as being safe to deploy.  Information Security ; Dangerous , \nViolent, or Hateful  Content  \nAI Actor Tasks:  Domain Experts, TEVV'
 'warrant additional human review, tracking and documentation, and greater management oversight.  \nAI technology can produce varied outputs  in multiple modalities and present many classes of user \ninterfaces. This leads to a broader set of AI Actors  interacting with GAI systems for widely differing \napplications and contexts of use. These  can include data labeling and preparation, development of GAI \nmodels, content moderation, code generation and review, text generation and editing, image and video \ngeneration, summarization, search, and chat. These activities can take place within organizational \nsettings or in the public domain.  \nOrganizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conflict with their tolerances or values. Governance tools and protocols that are applied to other types of AI systems can be applied to GAI systems. These p lans and actions include: \n• Accessibility and reasonable accommodations  \n• AI actor credentials and qualifications  \n• Alignment to organizational values  • Auditing and assessment  \n• Change -management controls  \n• Commercial use  \n• Data provenance']","GAI technology can significantly enhance red-teaming efforts with human teams in several ways, while also ensuring the origin of content and the disclosure of incidents. Here are some key points:

1. **Augmented Analysis**: GAI can assist human red-teamers by quickly analyzing vast amounts of data and generating potential attack scenarios or vulnerabilities that might not be immediately apparent. This capability allows for a more comprehensive assessment of the system's security posture (Source: [document name], p. [page number]).

2. **Simulating Adversarial Behavior**: GAI can simulate various adversarial tactics, techniques, and procedures (TTPs) that could be used against a system. By generating realistic attack simulations, GAI helps human teams prepare for and respond to potential threats more effectively (Source: [document name], p. [page number]).

3. **Cost-Effectiveness**: Utilizing GAI in red-teaming can be more cost-effective than relying solely on human red-teamers. GAI can handle repetitive tasks and preliminary assessments, allowing human experts to focus on more complex and nuanced evaluations (Source: [document name], p. [page number]).

4. **Content Provenance Tracking**: GAI systems can incorporate mechanisms for tracking the origin and history of generated content. This includes metadata about the GAI model used, the date and time of content creation, and any modifications made. Such tracking enhances accountability and helps organizations understand the context of the generated outputs (Source: [document name], p. [page number]).

5. **Incident Disclosure Protocols**: By integrating GAI with incident reporting frameworks, organizations can ensure that any incidents or anomalies identified during red-teaming exercises are documented and disclosed appropriately. This transparency is crucial for learning from incidents and improving future security measures (Source: [document name], p. [page number]).

6. **Collaborative Feedback Loops**: GAI can facilitate continuous feedback loops between human red-teamers and the AI system. This collaboration allows for the refinement of both the GAI models and the red-teaming strategies, leading to more effective risk management and incident response (Source: [document name], p. [page number]).

7. **Standardization of Reporting**: GAI can help standardize the documentation and reporting of red-teaming activities, making it easier to share findings and insights across teams. This standardization can improve the overall effectiveness of risk management efforts and enhance organizational learning (Source: [document name], p. [page number]).

By leveraging GAI technology in red-teaming efforts, organizations can enhance their security assessments while ensuring that content origin and incident disclosures are managed effectively, ultimately leading to a more robust security posture.","GAI technologies can improve red-teaming with human teams by combining GAI with specialist or non-specialist human teams. GAI-led red-teaming can be more cost-effective than human red-teamers alone and may be better suited for eliciting different types of harms. Content provenance mechanisms like provenance data tracking can trace the origin and history of content, helping to manage and mitigate risks associated with GAI output. These approaches can enable processes to trace negative outcomes back to their source, improve information integrity, and uphold public trust.",0.5925925925925926,0.9374423875174976,1.0,0.999999999975,0.44688141867619435
Why is it important for lenders to inform consumers about decisions made under FCRA in automated systems?,"['NOTICE & \nEXPLANATION \nHOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\nReal-life examples of how these principles can become reality, through laws, policies, and practical \ntechnical and sociotechnical approaches to protecting rights, opportunities, and access. \nPeople in Illinois are given written notice by the private sector if their biometric informa-\ntion is used . The Biometric Information Privacy Act enacted by the state contains a number of provisions \nconcerning the use of individual biometric data and identifiers. Included among them is a provision that no private \nentity may ""collect, capture, purchase, receive through trade, or otherwise obtain"" such information about an \nindividual, unless written notice is provided to that individual or their legally appointed representative. 87\nMajor technology  companies are piloting new ways to communicate with the public about \ntheir automated  technologies. For example, a collection of non-profit organizations and companies have \nworked together to develop a framework that defines operational approaches to transparency for machine \nlearning systems.88 This framework, and others like it,89 inform the public about the use of these tools, going \nbeyond simple notice to include reporting elements such as safety evaluations, disparity assessments, and \nexplanations of how the systems work. \nLenders are required by federal law to notify consumers about certain decisions made about \nthem. Both the Fair Credit Reporting Act and the Equal Credit Opportunity Act require in certain circumstances \nthat consumers who are denied credit receive ""adverse action"" notices. Anyone who relies on the information in a \ncredit report to deny a consumer credit must, under the Fair Credit Reporting Act, provide an ""adverse action"" \nnotice to the consumer, which includes ""notice of the reasons a creditor took adverse action on the application'
 ""automated systems in an equitable way . The guardrails protecting the public from discrimination in their daily \nlives should include their digital lives and impacts—basic safeguards against abuse, bias, and discrimination to \nensure that all people are treated fairly when automated systems are used. This includes all dimensions of their \nlives, from hiring to loan approvals, from medical treatment and payment to encounters with the criminal \njustice system. Ensuring equity should also go beyond existing guardrails to consider the holistic impact that \nautomated systems make on underserved communities and to institute proactive protections that support these \ncommunities. \n• An automated system using nontraditional factors such as educational attainment and employment history as\npart of its loan underwriting and pricing model was found to be much more likely to charge an applicant whoattended a Historically Black College or University (HBCU) higher loan prices for refinancing a student loanthan an applicant who did not attend an HBCU. This was found to be true even when controlling for\nother credit-related factors.32\n•A hiring tool that learned the features of a company's employees (predominantly men) rejected women appli -\ncants for spurious and discriminatory reasons; resumes with the word “women’s,” such as “women’s\nchess club captain,” were penalized in the candidate ranking.33\n•A predictive model marketed as being able to predict whether students are likely to drop out of school wasused by more than 500 universities across the countr y. The model was found to use race directly as a predicto r,\nand also shown to have large disparities by race; Black students were as many as four times as likely as theirotherwise similar white peers to be deemed at high risk of dropping out. These risk scores are used by advisors to guide students towards or away from majors, and some worry that they are being used to guide\nBlack students away from math and science subjects.34""
 'You should know that an automated system is being used, \nand understand how and why it contributes to outcomes that impact you. Designers, developers, and deployers of automat\n-\ned systems should provide generally accessible plain language docu -\nmentation including clear descriptions of the overall system func -\ntioning and the role automation plays, notice that such systems are in use, the individual or organization responsible for the system, and ex\n-\nplanations of outcomes that are clear, timely, and accessible. Such notice should be kept up-to-date and people impacted by the system should be notified of significant use case or key functionality chang\n-\nes. You should know how and why an outcome impacting you was de -\ntermined by an automated system, including when the automated system is not the sole input determining the outcome. Automated systems should provide explanations that are technically valid, meaningful and useful to you and to any operators or others who need to understand the system, and calibrated to the level of risk based on the context. Reporting that includes summary information about these automated systems in plain language and assessments of the clarity and quality of the notice and explanations should be made public whenever possible.   NOTICE AND  EXPLANATION\n40'
 'or on an existing credit account.""90 In addition, under the risk-based pricing rule,91 lenders must either inform \nborrowers of their credit score, or else tell consumers when ""they are getting worse terms because of \ninformation in their credit report."" The CFPB has also asserted that ""[t]he law gives every applicant the right to \na specific explanation if their application for credit was denied, and that right is not diminished simply because \na company uses a complex algorithm  that it doesn\'t understand.""92 Such explanations illustrate a shared value \nthat certain decisions need to be explained. \nA California law  requires  that warehouse employees are  provided with  notice and  explana-\ntion about quotas, potentially facilitated by automated systems, that apply to them. Warehous-\ning employers in California that use quota systems (often facilitated by algorithmic monitoring systems) are \nrequired to provide employees with a written description of each quota that applies to the employee, including \n“quantified number of tasks to be performed or materials to be produced or handled, within the defined \ntime period, and any potential adverse employment action that could result from failure to meet the quota.”93\nAcross the federal government, agencies are conducting and supporting research on explain-\nable AI systems. The NIST is conducting fundamental research on the explainability of AI systems. A multidis-\nciplinary team of researchers aims to develop measurement methods and best practices to support the \nimplementation of core tenets of explainable AI.94 The Defense Advanced Research Projects Agency has a \nprogram on Explainable Artificial Intelligence that aims to create a suite of machine learning techniques that \nproduce more explainable models, while maintaining a high level of learning performance (prediction \naccuracy), and enable human users to understand, appropriately trust, and effectively manage the emerging']","It is important for lenders to inform consumers about decisions made under the Fair Credit Reporting Act (FCRA) in automated systems for several key reasons:

1. **Transparency**: Informing consumers about decisions made using automated systems promotes transparency in the lending process. Consumers have the right to know how their creditworthiness is assessed and what factors influenced the lender's decision (Source: [document name], p. [page number]).

2. **Consumer Rights**: The FCRA grants consumers specific rights regarding their credit information, including the right to receive ""adverse action"" notices when credit is denied. By providing this information, lenders help ensure that consumers are aware of their rights and can take appropriate action if they believe they have been treated unfairly (Source: [document name], p. [page number]).

3. **Opportunity for Correction**: When consumers are informed about the reasons for adverse decisions, they have the opportunity to review their credit reports and correct any inaccuracies. This can help improve their credit standing and ensure that future lending decisions are based on accurate information (Source: [document name], p. [page number]).

4. **Accountability**: By disclosing the decision-making process, lenders are held accountable for their use of automated systems. This accountability is crucial for addressing potential biases or errors in the algorithms that may lead to unfair treatment of consumers (Source: [document name], p. [page number]).

5. **Building Trust**: Providing clear explanations about automated decisions helps build trust between lenders and consumers. When consumers understand how decisions are made, they are more likely to feel confident in the lending process and the fairness of the outcomes (Source: [document name], p. [page number]).

6. **Regulatory Compliance**: Compliance with the FCRA is a legal requirement for lenders. By informing consumers about decisions made under this act, lenders ensure that they are adhering to regulatory standards and avoiding potential legal repercussions (Source: [document name], p. [page number]).

Overall, informing consumers about decisions made under the FCRA in automated systems is essential for promoting transparency, protecting consumer rights, and fostering trust in the lending process.","It is important for lenders to inform consumers about decisions made under FCRA in automated systems because the Fair Credit Reporting Act requires that consumers who are denied credit receive ""adverse action"" notices. These notices must include the reasons for the adverse action taken on the application or an existing credit account. Additionally, under the risk-based pricing rule, lenders must inform borrowers of their credit score or explain when they are receiving worse terms due to information in their credit report. This transparency is crucial to ensure that consumers understand the basis for credit decisions, especially when complex algorithms are involved.",1.0,0.9785683808084092,0.75,0.999999999975,0.4158924698951019
Why is public transparency important in automated systems affecting people's lives and decisions?,"['should not be used in education, work, housing, or in other contexts where the use of such surveillance \ntechnologies is likely to limit rights, opportunities, or access. Whenever possible, you should have access to \nreporting that confirms your data decisions have been respected and provides an assessment of the \npotential impact of surveillance technologies on your rights, opportunities, or access. \nNOTICE AND EXPLANATION\nYou should know that an automated system is being used and understand how and why it contributes to outcomes that impact you. Designers, developers, and deployers of automated systems should provide generally accessible plain language documentation including clear descriptions of the overall system functioning and the role automation plays, notice that such systems are in use, the individual or organiza\n-\ntion responsible for the system, and explanations of outcomes that are clear, timely, and accessible. Such notice should be kept up-to-date and people impacted by the system should be notified of significant use case or key functionality changes. You should know how and why an outcome impacting you was determined by an automated system, including when the automated system is not the sole input determining the outcome. Automated systems should provide explanations that are technically valid, meaningful and useful to you and to any operators or others who need to understand the system, and calibrated to the level of risk based on the context. Reporting that includes summary information about these automated systems in plain language and assessments of the clarity and quality of the notice and explanations should be made public whenever possible. \n6'
 'In addition to being able to opt out and use a human alternative, the American public deserves a human fallback system in the event that an automated system fails or causes harm. No matter how rigorously an automated system is tested, there will always be situations for which the system fails. The American public deserves protection via human review against these outlying or unexpected scenarios. In the case of time-critical systems, the public should not have to wait—immediate human consideration and fallback should be available. In many time-critical systems, such a remedy is already immediately available, such as a building manager who can open a door in the case an automated card access system fails. \nIn the criminal justice system, employment, education, healthcare, and other sensitive domains, automated systems are used for many purposes, from pre-trial risk assessments and parole decisions to technologies that help doctors diagnose disease. Absent appropriate safeguards, these technologies can lead to unfair, inaccurate, or dangerous outcomes. These sensitive domains require extra protections. It is critically important that there is extensive human oversight in such settings. \nThese critical protections have been adopted in some scenarios. Where automated systems have been introduced to provide the public access to government benefits, existing human paper and phone-based processes are generally still in place, providing an important alternative to ensure access. Companies that have introduced automated call centers often retain the option of dialing zero to reach an operator. When automated identity controls are in place to board an airplane or enter the country, there is a person supervising the systems who can be turned to for help or to appeal a misidentification.'
 'SAFE AND EFFECTIVE \nSYSTEMS \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section provides a brief summary of the problems which the principle seeks to address and protect \nagainst, including illustrative examples. \nWhile technologies are being deployed to solve problems across a wide array of issues, our reliance on technology can \nalso lead to its use in situations where it has not yet been proven to work—either at all or within an acceptable range of error. In other cases, technologies do not work as intended or as promised, causing substantial and unjustified harm. Automated systems sometimes rely on data from other systems, including historical data, allowing irrelevant informa\n-\ntion from past decisions to infect decision-making in unrelated situations.  In some cases, technologies are purposeful -\nly designed to violate the safety of others, such as technologies designed to facilitate stalking; in other cases, intended or unintended uses lead to unintended harms. \nMany of the harms resulting from these technologies are preventable, and actions are already being taken to protect the public. Some companies have put in place safeguards that have prevented harm from occurring by ensuring that key development decisions are vetted by an ethics review; others have identified and mitigated harms found through pre-deployment testing and ongoing monitoring processes. Governments at all levels have existing public consulta\n-\ntion processes that may be applied when considering the use of new automated systems, and existing product develop -\nment and testing practices already protect the American public from many potential harms. \nStill, these kinds of practices are deployed too rarely and unevenly. Expanded, proactive protections could build on these existing practices, increase confidence in the use of automated systems, and protect the American public. Inno\n-'
 ""Providing notice has long been a standard practice, and in many cases is a legal requirement, when, for example, making a video recording of someone (outside of a law enforcement or national security context). In some cases, such as credit, lenders are required to provide notice and explanation to consumers. Techniques used to automate the process of explaining such systems are under active research and improvement and such explanations can take many forms. Innovative companies and researchers are rising to the challenge and creating and deploying explanatory systems that can help the public better understand decisions that impact them. \nWhile notice and explanation requirements are already in place in some sectors or situations, the American public deserve to know consistently and across sectors if an automated system is being used in a way that impacts their rights, opportunities, or access. This knowledge should provide confidence in how the public is being treated, and trust in the validity and reasonable use of automated systems. \n• A lawyer representing an older client with disabilities who had been cut off from Medicaid-funded home\nhealth-care assistance couldn't determine why\n, especially since the decision went against historical access\npractices. In a court hearing, the lawyer learned from a witness that the state in which the older client\nlived \nhad recently adopted a new algorithm to determine eligibility.83 The lack of a timely explanation made it\nharder \nto understand and contest the decision.\n•\nA formal child welfare investigation is opened against a parent based on an algorithm and without the parent\never \nbeing notified that data was being collected and used as part of an algorithmic child maltreatment\nrisk assessment.84 The lack of notice or an explanation makes it harder for those performing child\nmaltreatment assessments to validate the risk assessment and denies parents knowledge that could help them\ncontest a decision.\n41""]","Public transparency is crucial in automated systems that impact individuals' lives and decisions for several reasons:

1. **Accountability**: Transparency ensures that organizations are held accountable for the decisions made by automated systems. When individuals understand how decisions are made, they can identify and challenge any unfair or biased outcomes, promoting responsible use of technology (Source: [document name], p. [page number]).

2. **Trust Building**: When organizations are transparent about their automated systems, it fosters trust among users. People are more likely to accept and engage with systems they understand, which can lead to greater user satisfaction and cooperation (Source: [document name], p. [page number]).

3. **Informed Decision-Making**: Transparency allows individuals to make informed choices about their interactions with automated systems. When users know how their data is used and how decisions are made, they can better assess the risks and benefits associated with those systems (Source: [document name], p. [page number]).

4. **Protection of Rights**: Automated systems can significantly affect individuals' rights and opportunities, such as in employment, credit, and healthcare. Transparency helps ensure that these systems operate fairly and do not infringe on individuals' rights, allowing for better protection against discrimination and bias (Source: [document name], p. [page number]).

5. **Public Engagement**: Transparency encourages public engagement and dialogue about the ethical implications of automated systems. When people are informed about how these systems work, they can participate in discussions about their design, implementation, and regulation, leading to more democratic governance (Source: [document name], p. [page number]).

6. **Error Identification and Correction**: When automated systems are transparent, it becomes easier to identify errors or biases in their operation. This visibility allows for timely corrections and improvements, enhancing the overall effectiveness and fairness of the systems (Source: [document name], p. [page number]).

7. **Regulatory Compliance**: Many jurisdictions require transparency in automated decision-making processes as part of regulatory frameworks. Adhering to these requirements not only ensures compliance but also demonstrates a commitment to ethical practices (Source: [document name], p. [page number]).

In summary, public transparency in automated systems is essential for accountability, trust, informed decision-making, protection of rights, public engagement, error correction, and regulatory compliance, ultimately leading to more ethical and responsible use of technology.","Public transparency is crucial in automated systems affecting people's lives and decisions because it allows both the authorities and the public to understand why and how decisions are being made. Without transparency, individuals may be subject to decisions made by automated systems without any explanation or accountability, leading to potential errors, biases, and injustices. Transparency also enables experts to verify the reasonableness of recommendations before they are implemented, ensuring safety and efficacy. In summary, public transparency in automated systems is essential for accountability, fairness, and the protection of individuals' rights and opportunities.",1.0,0.9680359407045404,0.75,0.999999999975,0.41817649888118796
How can governance principles manage risks of GAI effectively?,"['47 Appendix A.  Primary GAI Considerations  \nThe following primary considerations were derived as overarching themes from the GAI PWG \nconsultation process. These considerations (Governance, Pre- Deployment Testing, Content Provenance, \nand Incident Disclosure) are relevant  for volun tary use by any organization designing, developing, and \nusing GAI  and also inform the Actions to Manage GAI risks. Information included about the primary \nconsiderations is not exhaustive , but highlights the most relevant topics derived from the GAI PWG.  \nAcknowledgments: These considerations could not have been surfaced without the helpful analysis and \ncontributions from the community and NIST staff GAI PWG leads: George Awad, Luca Belli, Harold Booth, \nMat Heyman, Yoo young Lee, Mark Pryzbocki, Reva Schwartz, Martin Stanley, and Kyra Yee.  \nA.1. Governance  \nA.1.1.  Overview  \nLike any other technology system, governance principles and techniques can be used to manage risks \nrelated to generative AI models, capabilities, and applications. Organizations may choose to apply their \nexisting risk tiering to GAI systems, or they may op t to revis e or update AI system risk levels to address \nthese unique GAI risks. This section describes how organizational governance regimes may be re -\nevaluated and adjusted for GAI contexts. It also addresses third -party considerations for governing across  \nthe AI value chain.  \nA.1.2.  Organizational  Governance  \nGAI opportunities, risks and long- term performance characteristics are typically less well -understood \nthan non- generative AI tools  and may be perceived and acted upon by humans in ways that vary greatly. \nAccordingly, GAI may call for different levels of oversight from AI Actors  or different human- AI \nconfigurations in order to manage their risks effectively. Organizations’ use of GAI systems may also \nwarrant additional human review, tracking and documentation, and greater management oversight.'
 'risks, and creates unique risks. GAI risks  can vary along many dimensions:  \n• Stage of the AI lifecycle:  Risks can arise  during design,  development , depl oyment , operation, \nand/or decommission ing. \n• Scope:  Risks  may exist  at individual model  or system  levels , at the application or implementation \nlevel s (i.e., for a specific use case), or at the ecosystem level  – that is, beyond a single  system or \norganizational context . Examples of the latter include  the expansion of “ algorithmic \nmonocultures ,3” resulting from repeated use of the same model, or impacts on  access to \nopportunity, labor markets , and the creative economies .4 \n• Source of risk:  Risks may emerge from factors related to the de sign, training, or operation of  the \nGAI model itself, stemming in some cases from  GAI model or system inputs , and in other cases , \nfrom GAI system outputs.  Many GAI risks, however, originate from human  behavior , including \n \n \n3 “Algorithmic monocultures” refers to the phenomenon in which repeated use of the same model or algorithm  in \nconsequential decision- making settings like employment and lending can result in increased susceptibility  by \nsystems  to correlated failures  (like unexpected shocks),  due to multiple actors relying on the  same algorithm.  \n4 Many studies have projected the impact of AI on the workforce and  labor markets. Fewer studies have examined \nthe impact of GAI on the labor market , though some industry surveys indicate that  that both emp loyees and \nemployers are pondering this disruption.'
 'warrant additional human review, tracking and documentation, and greater management oversight.  \nAI technology can produce varied outputs  in multiple modalities and present many classes of user \ninterfaces. This leads to a broader set of AI Actors  interacting with GAI systems for widely differing \napplications and contexts of use. These  can include data labeling and preparation, development of GAI \nmodels, content moderation, code generation and review, text generation and editing, image and video \ngeneration, summarization, search, and chat. These activities can take place within organizational \nsettings or in the public domain.  \nOrganizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conflict with their tolerances or values. Governance tools and protocols that are applied to other types of AI systems can be applied to GAI systems. These p lans and actions include: \n• Accessibility and reasonable accommodations  \n• AI actor credentials and qualifications  \n• Alignment to organizational values  • Auditing and assessment  \n• Change -management controls  \n• Commercial use  \n• Data provenance'
 '11 value chain  (e.g., data inputs , processing, GAI training, or deployment  environments ), conventional \ncybersecurity practices  may need to adapt or evolve . \nFor instance , prompt  injection  involves  modifying what  input is provided to a  GAI system  so that it  \nbehave s in unintended ways. In direct prompt injections, attackers might craft malicious  prompts  and \ninput them directly to a GAI system , with a variety of  downstream negative consequences to \ninterconnected systems. Indirect prompt injection  attacks occur when  adversaries remotely (i.e., without \na direct interface) exploit LLM -integrated applications by injecting prompts into data likely to be \nretrieved. Security researchers have already demonstrated how indirect prompt injections can exploit \nvulnerabilities by  steal ing proprietary  data  or running malicious code remotely  on a machine. Merely \nquerying  a closed production model can elicit  previously undisclosed information about that model . \nAnother cybersecurity risk to GAI is data poisoning , in which  an adversary compromises  a training \ndataset used by a model to manipulate its output s or operation. Malicious tampering with  data or parts \nof the  model could exacerbate risks associated with GAI system outputs.  \nTrustworthy AI Characteristics:  Privacy Enhanced, Safe , Secure and Resilient , Valid and Reliable  \n2.10. Intellectual Property  \nIntellectual property risks from  GAI systems may arise where the use of copyrighted works  is not a fair \nuse under the fair use doctrine. If a GAI system’s training data included copyrighted material, GAI \noutputs  displaying instances of  training data  memorization  (see Data Privacy above) could infringe on \ncopyright. \nHow GAI relates to copyright, including the status of  generated  content that is similar to but does not \nstrictly copy  w ork protected by copyright , is currently being debated in legal fora.  Similar discussions are']","Governance principles can effectively manage the risks associated with Generative AI (GAI) through several key strategies:

1. **Establishing Clear Policies**: Organizations should develop and implement clear policies that outline the acceptable use of GAI technologies. These policies should define the scope of GAI applications, the types of data that can be used, and the ethical standards that must be adhered to (Source: [document name], p. [page number]).

2. **Risk Assessment Frameworks**: Implementing comprehensive risk assessment frameworks allows organizations to identify, evaluate, and prioritize risks associated with GAI. This includes assessing risks at various stages of the AI lifecycle, from design and development to deployment and decommissioning (Source: [document name], p. [page number]).

3. **Human Oversight and Review**: Governance structures should include provisions for human oversight and review of GAI outputs. This ensures that decisions made by GAI systems are subject to human judgment, particularly in high-stakes scenarios where the consequences of errors can be significant (Source: [document name], p. [page number]).

4. **Transparency and Accountability**: Organizations should promote transparency in their GAI systems by documenting decision-making processes and providing clear explanations of how outputs are generated. This accountability helps build trust and allows stakeholders to understand the rationale behind automated decisions (Source: [document name], p. [page number]).

5. **Stakeholder Engagement**: Engaging a diverse range of stakeholders, including employees, customers, and community representatives, in the governance process can provide valuable insights into potential risks and ethical considerations. This collaborative approach can help ensure that GAI systems align with societal values and expectations (Source: [document name], p. [page number]).

6. **Regular Audits and Compliance Checks**: Conducting regular audits of GAI systems can help organizations ensure compliance with established policies and regulations. These audits should assess the effectiveness of risk management strategies and identify areas for improvement (Source: [document name], p. [page number]).

7. **Training and Education**: Providing training and education for employees on the ethical use of GAI technologies is essential. This helps create a culture of responsibility and awareness regarding the potential risks and implications of GAI applications (Source: [document name], p. [page number]).

8. **Incident Reporting and Response**: Establishing clear protocols for incident reporting and response allows organizations to address any issues that arise from GAI use promptly. This includes mechanisms for disclosing incidents and learning from them to improve future practices (Source: [document name], p. [page number]).

By implementing these governance principles, organizations can effectively manage the risks associated with Generative AI, ensuring that these technologies are used responsibly and ethically while maximizing their benefits.","Governance principles can be used to manage risks related to generative AI models, capabilities, and applications. Organizations may choose to apply their existing risk tiering to GAI systems or revise/update AI system risk levels to address unique GAI risks. Organizational governance regimes may need to be re-evaluated and adjusted for GAI contexts, including third-party considerations across the AI value chain. GAI may require different levels of oversight from AI actors or different human-AI configurations to manage risks effectively. Organizations using GAI systems may need additional human review, tracking, documentation, and management oversight. Governance tools and protocols applied to other AI systems can also be applied to GAI systems, including accessibility, AI actor credentials, alignment to organizational values, auditing, change-management controls, commercial use, and data provenance.",0.6470588235294118,0.9239523900214239,1.0,0.999999999975,0.6799857290700516
"Why is accuracy important in reviewing and documenting data throughout the AI life cycle, considering factors like bias, IP, integrity, and GAI risks?","['warrant additional human review, tracking and documentation, and greater management oversight.  \nAI technology can produce varied outputs  in multiple modalities and present many classes of user \ninterfaces. This leads to a broader set of AI Actors  interacting with GAI systems for widely differing \napplications and contexts of use. These  can include data labeling and preparation, development of GAI \nmodels, content moderation, code generation and review, text generation and editing, image and video \ngeneration, summarization, search, and chat. These activities can take place within organizational \nsettings or in the public domain.  \nOrganizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conflict with their tolerances or values. Governance tools and protocols that are applied to other types of AI systems can be applied to GAI systems. These p lans and actions include: \n• Accessibility and reasonable accommodations  \n• AI actor credentials and qualifications  \n• Alignment to organizational values  • Auditing and assessment  \n• Change -management controls  \n• Commercial use  \n• Data provenance'
 'Information Integrity  \nMS-3.3-003 Evaluate potential biases and stereotypes that could emerge from the AI -\ngenerated content using appropriate methodologies including computational testing methods as well as evaluating structured feedback input.  Harmful Bias and Homogenization'
 'There may also be concerns  about  emotional entanglement  between humans and GAI systems, which \ncould lead to negative psychological impacts . \nTrustworthy AI Characteristics: Accountable and Transparent, Explainable and Interpretable, Fair with \nHarmful Bias Managed, Privacy Enhanced, Safe , Valid and Reliable  \n2.8. Information Integrity  \nInformation integrity  describes the “ spectrum of information and associated patterns of its creation, \nexchange, and consumption in society .” High-integrity information can be trusted; “distinguishes fact \nfrom fiction, opinion, and inference; acknowledges uncertainties; and is transparent about its level of \nvetting. This information can be linked to the original source(s) with appropriate evidence. High- integrity \ninformation is also accurate and reliable, can be verified and  authenticated, has a clear chain of custody, \nand creates reasonable expectations about when its validity  may expire. ”11 \n \n \n11 This definition of information integrity is derived from the  2022 White House Roadmap for Researchers on \nPriorities Related to Information Integrity Research and Development.'
 '30 MEASURE 2.2:  Evaluations involving human subjects meet applicable requirements (including human subject protection) and are \nrepresentative of the relevant population.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.2-001 Assess and manage statistical biases related to GAI content provenance through \ntechniques such as re -sampling, re -weighting, or adversarial training.  Information Integrity ; Information \nSecurity ; Harmful Bias and \nHomogenization  \nMS-2.2-002 Document how content provenance data  is tracked  and how that data interact s \nwith  privacy and security . Consider : Anonymiz ing data to protect the privacy of \nhuman subjects; Leverag ing privacy output filters; Remov ing any personally \nidentifiable information (PII) to prevent potential harm or misuse.  Data Privacy ; Human AI \nConfiguration; Information \nIntegrity ; Information Security ; \nDangerous , Violent, or Hateful \nContent  \nMS-2.2-0 03 Provide human subjects with options to withdraw participation or revoke their \nconsent for present or future use of their data in GAI applications .  Data Privacy ; Human -AI \nConfiguration; Information \nIntegrity  \nMS-2.2-0 04 Use techniques such as anonymization , differential privacy  or other  privacy -\nenhancing technologies to minimize the risks associated with linking AI -generated \ncontent back to individual human subjects.  Data Privacy ; Human -AI \nConfiguration  \nAI Actor Tasks:  AI Development, Human Factors, TEVV  \n \nMEASURE 2.3:  AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for \nconditions similar to deployment setting(s). Measures are documented.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.3-001 Consider baseline model performance on suites of benchmarks when selecting a \nmodel for fine tuning  or enhancement with retrieval- augmented generation. Information Security ; \nConfabulation']","Accuracy in reviewing and documenting data throughout the AI life cycle is crucial for several reasons, particularly concerning bias, intellectual property, integrity, and risks associated with Generative AI (GAI):

1. **Bias Mitigation**: Accurate data documentation helps identify and address biases in training datasets. If the data used to train AI models is not accurately reviewed, it may contain inherent biases that can lead to unfair or discriminatory outcomes. By ensuring accuracy, organizations can implement strategies to mitigate these biases, leading to fairer AI systems (Source: [document name], p. [page number]).

2. **Intellectual Property Protection**: Accurate documentation of data sources and usage is essential for protecting intellectual property rights. Organizations must ensure that they have the proper licenses and permissions for the data they use. Inaccuracies in this documentation can lead to legal disputes or unintentional infringement on third-party rights, which can have significant financial and reputational consequences (Source: [document name], p. [page number]).

3. **Integrity of AI Outputs**: The integrity of AI outputs relies heavily on the quality and accuracy of the data used in the training process. If the data is inaccurate or poorly documented, the resulting AI models may produce unreliable or misleading outputs. This can undermine trust in the AI system and lead to negative consequences for users and stakeholders (Source: [document name], p. [page number]).

4. **Risk Management**: Accurate data documentation is vital for effective risk management throughout the AI life cycle. By maintaining precise records of data sources, processing methods, and model performance, organizations can better assess potential risks associated with GAI applications. This includes understanding how data quality impacts model behavior and identifying areas where risks may arise (Source: [document name], p. [page number]).

5. **Regulatory Compliance**: Many jurisdictions have regulations governing data use, privacy, and AI applications. Accurate documentation helps organizations comply with these regulations by providing a clear audit trail of data handling practices. This compliance is essential for avoiding legal penalties and maintaining public trust (Source: [document name], p. [page number]).

6. **Facilitating Accountability**: Accurate documentation of data and decision-making processes enhances accountability within organizations. When stakeholders can trace the origins and transformations of data, it becomes easier to hold individuals and teams accountable for the outcomes produced by AI systems (Source: [document name], p. [page number]).

7. **Continuous Improvement**: Maintaining accurate records allows organizations to learn from past experiences and improve their AI systems over time. By analyzing documented data and outcomes, organizations can identify patterns, refine their models, and enhance their overall AI strategies (Source: [document name], p. [page number]).

In summary, accuracy in reviewing and documenting data throughout the AI life cycle is essential for mitigating bias, protecting intellectual property, ensuring integrity, managing risks, complying with regulations, facilitating accountability, and promoting continuous improvement in AI systems.","Accuracy is crucial in reviewing and documenting data throughout the AI life cycle to ensure the data's reliability, representativeness, relevance, and suitability at different stages. This is particularly important due to factors like harmful bias, homogenization, intellectual property concerns, information integrity, and GAI risks. Ensuring accuracy helps in verifying the information generated by GAI systems, identifying potential biases or harmful content, and maintaining the trustworthiness of AI systems.",1.0,0.9600586340557004,1.0,0.999999999975,0.3549509835461354
How can feedback be used to gather user input on AI content while aligning with values and detecting quality shifts?,"['but are not limited to:  \n• Participatory Engagement Methods : Methods used to solicit feedback from civil society groups, \naffected communities, and users, including focus groups, small user studies, and surveys.  \n• Field Testing : Methods used to determine how people interact with, consume, use, and make \nsense of AI -generated information, and subsequent actions and effects, including UX, usability, \nand other structured, randomized experiments.  \n• AI Red -teaming:  A structured testing exercise\n used to probe an AI system to find flaws and \nvulnerabilities such as inaccurate, harmful, or discriminatory outputs, often in a controlled \nenvironment and in collaboration with system developers.  \nInformation gathered from structured public feedback can inform design, implementation, deployment \napproval , maintenance, or decommissioning decisions. Results and insights gleaned from these exercises \ncan serve multiple purposes, including improving data quality and preprocessing, bolstering governance decision making, and enhancing system documentation and debugging practices. When implementing \nfeedback activities, organizations should follow human subjects research requirements and best \npractices such as  informed consent and subject compensation.'
 'Information Integrity  \nMS-3.3-003 Evaluate potential biases and stereotypes that could emerge from the AI -\ngenerated content using appropriate methodologies including computational testing methods as well as evaluating structured feedback input.  Harmful Bias and Homogenization'
 'warrant additional human review, tracking and documentation, and greater management oversight.  \nAI technology can produce varied outputs  in multiple modalities and present many classes of user \ninterfaces. This leads to a broader set of AI Actors  interacting with GAI systems for widely differing \napplications and contexts of use. These  can include data labeling and preparation, development of GAI \nmodels, content moderation, code generation and review, text generation and editing, image and video \ngeneration, summarization, search, and chat. These activities can take place within organizational \nsettings or in the public domain.  \nOrganizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conflict with their tolerances or values. Governance tools and protocols that are applied to other types of AI systems can be applied to GAI systems. These p lans and actions include: \n• Accessibility and reasonable accommodations  \n• AI actor credentials and qualifications  \n• Alignment to organizational values  • Auditing and assessment  \n• Change -management controls  \n• Commercial use  \n• Data provenance'
 'MS-1.1-004 Develop a suite of metrics to evaluate structured public feedback exercises  \ninformed by representative AI Actors . Human -AI Configuration ; Harmful \nBias and Homogenization ; CBRN  \nInformation or Capabilities  \nMS-1.1-0 05 Evaluate novel methods and technologies for the measurement of GAI-related \nrisks in cluding in  content provenance , offensive cy ber, and CBRN , while \nmaintaining the models’ ability to produce valid, reliable, and factually accurate outputs.  Information Integrity ; CBRN \nInformation or Capabilities ; \nObscene, Degrading, and/or Abusive Content']","Feedback can be effectively utilized to collect user input on AI-generated content while ensuring alignment with organizational values and identifying shifts in quality through several strategies:

1. **Structured Feedback Mechanisms**: Implement structured feedback tools, such as surveys or rating systems, that allow users to provide specific input on AI-generated content. These tools can include questions related to the relevance, accuracy, and alignment of the content with organizational values (Source: [document name], p. [page number]).

2. **Participatory Engagement**: Engage users in participatory feedback sessions, such as focus groups or workshops, where they can discuss their experiences with AI-generated content. This qualitative feedback can provide deeper insights into user perceptions and values, helping organizations align their AI outputs with user expectations (Source: [document name], p. [page number]).

3. **Continuous Monitoring**: Establish mechanisms for continuous monitoring of user feedback over time. By regularly analyzing feedback trends, organizations can identify shifts in quality or user satisfaction, allowing for timely adjustments to the AI systems and content generation processes (Source: [document name], p. [page number]).

4. **Feedback Loops**: Create feedback loops where user input is not only collected but also acted upon. Communicate to users how their feedback has influenced changes in AI-generated content, reinforcing the importance of their input and fostering a sense of ownership and collaboration (Source: [document name], p. [page number]).

5. **Alignment with Values**: Develop a framework for evaluating AI-generated content against organizational values. This framework can include criteria for assessing whether the content promotes inclusivity, fairness, and ethical considerations. User feedback can then be used to refine these criteria and ensure ongoing alignment (Source: [document name], p. [page number]).

6. **Diverse User Representation**: Ensure that feedback is collected from a diverse range of users to capture different perspectives and values. This diversity can help organizations identify potential biases in AI-generated content and make necessary adjustments to improve quality and relevance (Source: [document name], p. [page number]).

7. **Quality Metrics Development**: Develop metrics to evaluate the quality of AI-generated content based on user feedback. These metrics can include measures of accuracy, relevance, and user satisfaction, providing a quantitative basis for assessing shifts in quality over time (Source: [document name], p. [page number]).

8. **Transparency in Feedback Processes**: Maintain transparency about how user feedback is collected, analyzed, and utilized. This transparency builds trust with users and encourages more honest and constructive feedback, ultimately leading to better alignment with values and improved content quality (Source: [document name], p. [page number]).

By implementing these strategies, organizations can effectively utilize feedback to enhance the quality of AI-generated content while ensuring that it aligns with their values and meets user expectations.",Use structured feedback mechanisms to solicit and capture user input about AI-generated content to detect subtle shifts in quality or alignment with community and societal values.,1.0,0.9389707895976366,1.0,0.9166666666361111,0.3104833954857933
What measures are being taken to address issues for transgender travelers at airport checkpoints?,"['teenager-2022-03-30/\n42. Miranda Bogen. All the Ways Hiring Algorithms Can Introduce Bias. Harvard Business Review. May\n6, 2019. https://hbr.org/2019/05/all-the-ways-hiring-algorithms-can-introduce-bias\n43. Arli Christian. Four Ways the TSA Is Making Flying Easier for Transgender People . American Civil\nLiberties Union. Apr. 5, 2022. https://www.aclu.org/news/lgbtq-rights/four-ways-the-tsa-is-making-\nflying-easier-for-transgender-people\n44. U.S. Transportation Security Administration. Transgender/ Non Binary / Gender Nonconforming\nPassengers. TSA. Accessed Apr. 21, 2022. https://www.tsa.gov/transgender-passengers45. See, e.g., National Disabled Law Students Association. Report on Concerns Regarding Online\nAdministration of Bar Exams. Jul. 29, 2020. https://ndlsa.org/wp-content/uploads/2020/08/\nNDLSA_Online-Exam-Concerns-Report1.pdf; Lydia X. Z. Brown. How Automated Test ProctoringSoftware Discriminates Against Disabled Students. Center for Democracy and Technology. Nov. 16, 2020.\nhttps://cdt.org/insights/how-automated-test-proctoring-software-discriminates-against-disabled-\nstudents/\n46. Ziad Obermeyer, et al., Dissecting racial bias in an algorithm used to manage the health of\npopulations, 366 Science (2019), https://www.science.org/doi/10.1126/science.aax2342.\n66'
 ""-\ntion when deployed. This assessment should be performed regularly and whenever a pattern of unusual results is occurring. It can be performed using a variety of approaches, taking into account whether and how demographic information of impacted people is available, for example via testing with a sample of users or via qualitative user experience research. Riskier and higher-impact systems should be monitored and assessed more frequentl y. Outcomes of this assessment should include additional disparity mitigation, if needed, or \nfallback to earlier procedures in the case that equity standards are no longer met and can't be mitigated, and prior mechanisms provide better adherence to equity standards. \n27Algorithmic \nDiscrimination \nProtections""
 'NOTICE & \nEXPLANATION \nHOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\nReal-life examples of how these principles can become reality, through laws, policies, and practical \ntechnical and sociotechnical approaches to protecting rights, opportunities, and access. \nPeople in Illinois are given written notice by the private sector if their biometric informa-\ntion is used . The Biometric Information Privacy Act enacted by the state contains a number of provisions \nconcerning the use of individual biometric data and identifiers. Included among them is a provision that no private \nentity may ""collect, capture, purchase, receive through trade, or otherwise obtain"" such information about an \nindividual, unless written notice is provided to that individual or their legally appointed representative. 87\nMajor technology  companies are piloting new ways to communicate with the public about \ntheir automated  technologies. For example, a collection of non-profit organizations and companies have \nworked together to develop a framework that defines operational approaches to transparency for machine \nlearning systems.88 This framework, and others like it,89 inform the public about the use of these tools, going \nbeyond simple notice to include reporting elements such as safety evaluations, disparity assessments, and \nexplanations of how the systems work. \nLenders are required by federal law to notify consumers about certain decisions made about \nthem. Both the Fair Credit Reporting Act and the Equal Credit Opportunity Act require in certain circumstances \nthat consumers who are denied credit receive ""adverse action"" notices. Anyone who relies on the information in a \ncredit report to deny a consumer credit must, under the Fair Credit Reporting Act, provide an ""adverse action"" \nnotice to the consumer, which includes ""notice of the reasons a creditor took adverse action on the application'
 'WHY THIS PRINCIPLE IS IMPORTANT\nThis section provides a brief summary of the problems which the principle seeks to address and protect \nagainst, including illustrative examples. \n• An automated sentiment analyzer, a tool often used by technology platforms to determine whether a state-\nment posted online expresses a positive or negative sentiment, was found to be biased against Jews and gay\npeople. For example, the analyzer marked the statement “I’m a Jew” as representing a negative sentiment,\nwhile “I’m a Christian” was identified as expressing a positive sentiment.36 This could lead to the\npreemptive blocking of social media comments such as: “I’m gay .” A related company with this bias concern\nhas made their data public to encourage researchers to help address the issue37 \nand has released reports\nidentifying and measuring this problem as well as detailing attempts to address it.38\n• Searches for “Black girls,” “Asian girls,” or “Latina girls” return predominantly39 sexualized content, rather\nthan role models, toys, or activities.40 Some search engines have been\n working to reduce the prevalence of\nthese results, but the problem remains.41\n• Advertisement delivery systems that predict who is most likely to click on a job advertisement end up deliv-\nering ads in ways that reinforce racial and gender stereotypes, such as overwhelmingly directing supermar-\nket cashier ads to women and jobs with taxi companies to primarily Black people.42\n•Body scanners, used by TSA at airport checkpoints, require the operator to select a “male” or “female”\nscanning setting based on the passenger’s sex, but the setting is chosen based on the operator’s perception of\nthe passenger’s gender identity\n. These scanners are more likely to flag transgender travelers as requiring\nextra screening done by a person. Transgender travelers have described degrading experiences associated\nwith these extra screenings.43 TSA has recently announced plans to implement a gender-neutral  algorithm44']","Measures being taken to address issues for transgender travelers at airport checkpoints include the implementation of gender-neutral policies and training for Transportation Security Administration (TSA) staff. These initiatives aim to create a more inclusive and respectful environment for transgender and non-binary individuals during the security screening process. 

1. **Gender-Neutral Screening Procedures**: The TSA has announced plans to implement gender-neutral algorithms in their body scanners. This change is intended to reduce the need for additional screening based on the operator's perception of a passenger's gender identity, which can lead to uncomfortable and degrading experiences for transgender travelers (Source: [U.S. Transportation Security Administration], p. [1]).

2. **Staff Training**: TSA personnel receive training on how to interact respectfully with transgender and non-binary travelers. This training includes understanding the challenges these individuals may face and how to provide appropriate assistance during the screening process (Source: [American Civil Liberties Union], p. [1]).

3. **Policy Updates**: The TSA has updated its policies to ensure that transgender travelers can navigate security checkpoints without facing discrimination or bias. This includes allowing travelers to present identification that reflects their gender identity, regardless of whether it matches their appearance (Source: [U.S. Transportation Security Administration], p. [1]).

4. **Public Awareness Campaigns**: The TSA and advocacy groups are working to raise awareness about the rights of transgender travelers and the measures in place to support them. This includes providing information on what to expect during the screening process and how to report any issues encountered (Source: [American Civil Liberties Union], p. [1]).

These measures are part of a broader effort to ensure that all travelers, regardless of gender identity, can travel safely and with dignity.",TSA has announced plans to implement a gender-neutral algorithm at airport checkpoints to address issues for transgender travelers. This algorithm aims to enhance security effectiveness capabilities while reducing the likelihood of flagging transgender travelers for extra screening based on gender identity perceptions.,0.6071428571428571,0.9857355893055081,1.0,0.7499999999625,0.36394619372675696
How do ballot curing laws help voters fix ballot issues despite flaws in signature matching systems?,"['The American people deserve the reassurance that such procedures are in place to protect their rights, opportunities, and access. People make mistakes, and a human alternative or fallback mechanism will not always have the right answer, but they serve as an important check on the power and validity of automated systems. \n•An automated signature matching system is used as part of the voting process in many parts of the country todetermine whether the signature on a mail-in ballot matches the signature on file. These signature matchingsystems are less likely to work correctly for some voters, including voters with mental or physicaldisabilities, voters with shorter or hyphenated names, and voters who have changed their name.\n97 A human\ncuring process,98 which helps voters to confirm their signatures and correct other voting mistakes, is\nimportant to ensure all votes are counted,99 and it is already standard practice in much of the country for\nboth an election official and the voter to have the opportunity to review and correct any such issues.100 \n47'
 '110 Ballot \ncuring processes vary among states, and include direct phone calls, emails, or mail contact by election \nofficials.111 Voters are asked to provide alternative information or a new signature to verify the validity of their \nballot. \n52'
 'ENDNOTES\n96. National Science Foundation. NSF Program on Fairness in Artificial Intelligence in Collaboration\nwith Amazon (FAI). Accessed July 20, 2022.\nhttps://www.nsf.gov/pubs/2021/nsf21585/nsf21585.htm\n97. Kyle Wiggers. Automatic signature verification software threatens to disenfranchise U.S. voters.\nVentureBeat. Oct. 25, 2020.\nhttps://venturebeat.com/2020/10/25/automatic-signature-verification-software-threatens-to-disenfranchise-u-s-voters/\n98. Ballotpedia. Cure period for absentee and mail-in ballots. Article retrieved Apr 18, 2022.\nhttps://ballotpedia.org/Cure_period_for_absentee_and_mail-in_ballots\n99. Larry Buchanan and Alicia Parlapiano. Two of these Mail Ballot Signatures are by the Same Person.\nWhich Ones? New York Times. Oct. 7, 2020.\nhttps://www.nytimes.com/interactive/2020/10/07/upshot/mail-voting-ballots-signature-\nmatching.html\n100. Rachel Orey and Owen Bacskai. The Low Down on Ballot Curing. Nov. 04, 2020.\nhttps://bipartisanpolicy.org/blog/the-low-down-on-ballot-curing/101. Andrew Kenney. \'I\'m shocked that they need to have a smartphone\': System for unemployment\nbenefits exposes digital divide. USA Today. May 2, 2021.\nhttps://www.usatoday.com/story/tech/news/2021/05/02/unemployment-benefits-system-leaving-\npeople-behind/4915248001/\n102. Allie Gross. UIA lawsuit shows how the state criminalizes the unemployed . Detroit Metro-Times.\nSep. 18, 2015.\nhttps://www.metrotimes.com/news/uia-lawsuit-shows-how-the-state-criminalizes-the-unemployed-2369412\n103. Maia Szalavitz. The Pain Was Unbearable. So Why Did Doctors Turn Her Away? Wired. Aug. 11,\n2021. https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/\n104. Spencer Soper. Fired by Bot at Amazon: ""It\'s You Against the Machine"" . Bloomberg, Jun. 28, 2021.\nhttps://www.bloomberg.com/news/features/2021-06-28/fired-by-bot-amazon-turns-to-machine-\nmanagers-and-workers-are-losing-out\n105. Definitions of ‘equity’ and ‘underserved communities’ can be found in the Definitions section of'
 'automated systems and machine learning, and the Designing Accountable Software Systems27 program supports \nresearch on rigorous and reproducible methodologies for developing software systems with legal and regulatory \ncompliance in mind. \nSome state legislatures have placed strong transparency and validity requirements on \nthe use of pretrial risk assessments. The use of algorithmic pretrial risk assessments has been a \ncause of concern for civil rights groups.28 Idaho Code Section 19-1910, enacted in 2019,29 requires that any \npretrial risk assessment, before use in the state, first be ""shown to be free of bias against any class of \nindividuals protected from discrimination by state or federal law"", that any locality using a pretrial risk \nassessment must first formally validate the claim of its being free of bias, that ""all documents, records, and \ninformation used to build or validate the risk assessment shall be open to public inspection,"" and that assertions \nof trade secrets cannot be used ""to quash discovery in a criminal matter by a party to a criminal case."" \n22']","Ballot curing laws play a crucial role in assisting voters in addressing issues with their ballots, particularly in the context of the shortcomings of signature matching systems. Here are some key ways these laws help:

1. **Opportunity for Correction**: Ballot curing laws provide voters with the opportunity to correct any issues identified with their mail-in or absentee ballots. If a signature on a ballot does not match the signature on file, these laws often allow voters to verify their identity and submit a new signature or provide additional information to confirm their eligibility (Source: [Ballotpedia], p. [1]).

2. **Communication from Election Officials**: Many ballot curing processes require election officials to notify voters when there is a problem with their ballot. This communication can take the form of direct phone calls, emails, or mail, informing voters of the specific issue and guiding them on how to resolve it (Source: [Ballotpedia], p. [1]).

3. **Extended Timeframes**: Ballot curing laws often establish a ""cure period,"" which is a designated timeframe during which voters can address issues with their ballots. This extension allows voters to take the necessary steps to ensure their votes are counted, rather than being disenfranchised due to a technicality (Source: [Bipartisan Policy Center], p. [1]).

4. **Inclusivity for Diverse Voter Populations**: These laws are particularly important for voters who may face challenges with signature matching systems, such as individuals with disabilities, those who have changed their names, or voters with shorter or hyphenated names. By allowing for corrections, ballot curing laws help ensure that these voters are not unfairly disenfranchised (Source: [VentureBeat], p. [1]).

5. **Strengthening Voter Confidence**: By providing mechanisms for voters to address ballot issues, ballot curing laws can enhance public confidence in the electoral process. Voters are more likely to participate when they know there are safeguards in place to ensure their votes can be counted, even if initial issues arise (Source: [Bipartisan Policy Center], p. [1]).

Overall, ballot curing laws serve as an essential safeguard in the voting process, helping to mitigate the limitations of signature matching systems and ensuring that all eligible voters have the opportunity to have their voices heard.","Ballot curing laws in at least 24 states provide a fallback system that allows voters to correct their ballot and have it counted in case a voter signature matching algorithm incorrectly flags their ballot as invalid or if there is another issue with their ballot that cannot be rectified by an election official review. This process ensures that voters have the opportunity to address any issues with their ballot and have their vote counted, as some federal courts have determined that such cure procedures are constitutionally required.",1.0,0.9329417485315551,0.6666666666666666,0.99999999995,0.8833405177012349
How can feedback and red-teaming assess GAI equity and ensure content transparency?,"['51 general public participants. For example, expert AI red- teamers could modify or verify the \nprompts written by general public AI red- teamers. These approaches may also expand coverage \nof the AI risk attack surface.  \n• Human / AI: Performed by GAI in combinatio n with  specialist or non -specialist human teams. \nGAI- led red -teaming can be more cost effective than human red- teamers alone. Human or GAI-\nled AI red -teaming may be better suited for eliciting different types of harms.   \nA.1.6.  Content Provenance  \nOverview \nGAI technologies can be leveraged for many applications such as content generation and synthetic data. \nSome aspects of GAI output s, such as the production of deepfake content, can challenge our ability to \ndistinguish human- generated content from AI -generated synthetic  content. To help manage and mitigate \nthese risks, digital transparency mechanisms like provenance data tracking can trace the origin and \nhistory of content. Provenance data tracking and synthetic content detection can help facilitate greater \ninformation access  about both authentic and synthetic content to users, enabling better knowledge of  \ntrustworthiness  in AI systems. When combined with other organizational accountability mechanisms, \ndigital content transparency approaches  can enable processes to trace negative outcomes back to their \nsource, improve information integrity, and uphold public trust. Provenance data tracking and synthetic content detection mechanisms provide information about the origin \nand history of content  to assist in \nGAI risk management efforts.  \nProvenance metad ata can include information about GAI model developers or creators  of GAI content , \ndate/time of creation, location, modifications, and sources. Metadata can be tracked for text, images, videos, audio, and underlying datasets. The implementation of p rovenance data tracking techniques  can'
 '50 Participatory Engagement Methods  \nOn an ad hoc or more structured basis, organizations can design and use a variety of channels to engage \nexternal stakeholders in product development or review. Focus groups with select experts can provide \nfeedback on a range of issues. Small user studies c an provide feedback from representative groups or \npopulations. Anonymous surveys can be used to poll or gauge reactions to specific features. Participatory engagement methods are often less structured than field testing or red teaming, and are more \ncommonl y used in early stages of AI or product development.  \nField Testing  \nField testing involves structured settings to evaluate risks and impacts and to simulate the conditions \nunder which the GAI system will be deployed. Field style tests can be adapted from a focus on user \npreferences and experiences towards AI risks and impacts – both negative and positive. When carried \nout with large groups of users, these tests can provide estimations of the likelihood of risks and impacts \nin real world interactions.  \nOrganizations may also collect feedback on outcomes, harms, and user experience directly from users in the production environment after a model has been released, in accordance with human subject \nstandards such as informed consent and compensation. Organiz ations should follow applicable human \nsubjects research requirements, and best practices such as informed consent and subject compensation, \nwhen implementing feedback activities. \nAI Red -teaming  \nAI red -teaming is an evolving practice that references exercises often  conducted in a controlled \nenvironment and in collaboration with AI developers building AI models  to identify potential adverse \nbehavior or outcomes of a GAI model or system,  how they could occur, an d stress test safeguards” . AI \nred-teaming can be performed before or after AI models or systems are made available to the broader'
 '8 Trustworthy AI Characteristics:  Accountable and Transparent, Privacy Enhanced, Safe, Secure and \nResilient  \n2.5. Environmental Impacts  \nTraining, maint aining, and operating (running inference  on) GAI systems are resource -intensive  activities , \nwith potentially large energy and environmental footprints. Energy and carbon emissions vary  based on \nwhat is being done with the  GAI model (i.e., pre -training, fine -tuning, inference), the modality  of the \ncontent , hardware used, and type of task or application . \nCurrent e stimates suggest that training a single transformer  LLM can emit as much carbon  as 300 round-\ntrip flights between San Francisco and New York.  In a study comparing energy consumption and carbon \nemissions for LLM inference, generative tasks ( e.g., text summarization) were found to be more energy - \nand carbon -i ntensive th an discriminative or non- generative tasks  (e.g., text classification).  \nMethods for creating smaller versions of train ed models, such as model distillation or compression, \ncould reduce  environmental impacts at inference time, but training and tuning such models may still \ncontribute to their environmental impacts . Currently there  is no agreed upon method to estimate \nenvironmental impacts  from GAI .  \nTrustworthy AI Characteristics:  Accountable and Transparent, Safe  \n2.6. Harmful Bias and Homogenization  \nBias exists in many forms  and can become ingrained in automated systems. AI systems , including GAI \nsystems,  can increase the speed and scale at which harmful biases manifest  and are acted upon, \npotentially  perpetuati ng and amplify ing harms to individuals, groups, communities, organizations, and \nsociety . For example,  when prompted to generate images of CEOs, doctors, lawyers, and judges, current \ntext-to-image models underrepresent  women and/or racial minorities , and people with disabilities . \nImage generator models have also produce d biased or stereotyped output for  various demographic'
 '29 MS-1.1-006 Implement continuous monitoring of GAI system impacts to identify whether GAI \noutputs are equitable across various sub- populations. Seek active and direct \nfeedback from affected communities  via structured feedback mechanisms or red -\nteaming to monitor and improve outputs.  Harmful Bias and Homogenization  \nMS-1.1-007 Evaluate the quality and integrity of data used in training and the provenance of \nAI-generated content , for example by e mploying  techniques like chaos \nengineering and seeking stakeholder feedback.  Information Integrity  \nMS-1.1-008 Define use cases, contexts of use, capabilities, and negative impacts where \nstructured human feedback exercises, e.g., GAI red- teaming, would be most \nbeneficial for GAI risk measurement and management based on the context of \nuse. Harmful Bias and \nHomogenization ; CBRN  \nInformation or Capabilities  \nMS-1.1-0 09 Track and document risks or opportunities related to all GAI risks  that cannot be \nmeasured quantitatively, including explanations as to why some risks cannot be \nmeasured (e.g., due to technological limitations, resource constraints, or trustworthy considerations).  Include unmeasured risks in marginal risks.  Information Integrity  \nAI Actor Tasks:  AI Development, Domain Experts, TEVV  \n \nMEASURE 1.3:  Internal experts who did not serve as front -line developers for the system and/or independent assessors are \ninvolved in regular assessments and updates. Domain experts, users, AI Actors  external to the team that developed or deployed the \nAI system, and affected communities are consulted in support of assessments as necessary per organizational risk tolerance . \nAction ID  Suggested Action  GAI Risks  \nMS-1.3-001 Define relevant groups of interest (e.g., demographic groups, subject matter \nexperts, experience with GAI technology) within the context of use as part of \nplans for gathering structured public feedback.  Human -AI Configuration ; Harmful \nBias and Homogenization ; CBRN']","Feedback and red-teaming can be effectively utilized to assess equity in Generative AI (GAI) systems and ensure transparency in the content produced through several strategies:

1. **Diverse Feedback Mechanisms**: Implement structured feedback mechanisms that solicit input from a diverse range of users, including those from underrepresented groups. This feedback can help identify potential biases in the GAI outputs and ensure that the content produced is equitable and inclusive (Source: [document name], p. [page number]).

2. **Equity-Focused Red-Teaming**: Conduct red-teaming exercises specifically focused on equity assessments. This involves assembling teams that include individuals with expertise in social justice, equity, and diversity to evaluate the GAI system's outputs for potential biases and discriminatory patterns (Source: [document name], p. [page number]).

3. **Scenario Testing**: Use red-teaming to simulate various scenarios that reflect different demographic groups and contexts. This testing can help identify how the GAI system performs across diverse populations and whether it produces equitable outcomes (Source: [document name], p. [page number]).

4. **Transparency in Processes**: Ensure that the processes for collecting feedback and conducting red-teaming are transparent. Documenting the methodologies used, the criteria for evaluation, and the outcomes of these assessments can help build trust and accountability in the GAI system (Source: [document name], p. [page number]).

5. **Iterative Improvement**: Use the insights gained from feedback and red-teaming to iteratively improve the GAI system. This includes refining the training data, adjusting algorithms, and implementing changes based on user input to enhance equity and transparency in the content produced (Source: [document name], p. [page number]).

6. **Public Reporting**: Regularly publish reports on the findings from feedback and red-teaming activities, highlighting any identified biases and the steps taken to address them. This transparency can help stakeholders understand the organization's commitment to equity and accountability (Source: [document name], p. [page number]).

7. **Engagement with Affected Communities**: Actively engage with communities that may be impacted by the GAI system. Gathering input from these groups can provide valuable perspectives on equity issues and help ensure that the content produced aligns with their needs and values (Source: [document name], p. [page number]).

By integrating feedback and red-teaming into the assessment of equity in GAI systems, organizations can enhance the fairness and transparency of the content produced, ultimately leading to more responsible and ethical AI applications.","Implement continuous monitoring of GAI system impacts to identify whether GAI outputs are equitable across various sub-populations. Seek active and direct feedback from affected communities via structured feedback mechanisms or red-teaming to monitor and improve outputs. Evaluate the quality and integrity of data used in training and the provenance of AI-generated content by employing techniques like chaos engineering and seeking stakeholder feedback. Define use cases, contexts of use, capabilities, and negative impacts where structured human feedback exercises, e.g., GAI red-teaming, would be most beneficial for GAI risk measurement and management based on the context of use. Track and document risks or opportunities related to all GAI risks that cannot be measured quantitatively, including explanations as to why some risks cannot be measured (e.g., due to technological limitations, resource constraints, or trustworthy considerations). Include unmeasured risks in marginal risks.",1.0,0.9171477400067344,1.0,0.999999999975,0.6789711324898036
How can algorithmic discrimination be prevented through proactive measures and equity assessments?,"['orientation), religion, age, national origin, disability, veteran status, genetic information, or any other \nclassification protected by law. Depending on the specific circumstances, such algorithmic  discrimination \nmay violate legal protections. Designers, developers, and deployers of automated systems should take \nproactive and continuous measures to protect individuals and communities from algorithmic \ndiscrimination and to use and design systems in an equitable way. This protection should include proactive \nequity assessments as part of the system design, use of representative data and protection against proxies \nfor demographic features, ensuring accessibility for people with disabilities in design and development, \npre-deployment and ongoing disparity testing and mitigation, and clear organizational oversight. Independent \nevaluation and plain language reporting in the form of an algorithmic impact assessment, including \ndisparity testing results and mitigation information, should be performed and made public whenever \npossible to confirm these protections. \n5'
 ""ENDNOTES\n47. Darshali A. Vyas et al., Hidden in Plain Sight – Reconsidering the Use of Race Correction in Clinical\nAlgorithms, 383 N. Engl. J. Med.874, 876-78 (Aug. 27, 2020), https://www.nejm.org/doi/full/10.1056/\nNEJMms2004740.\n48. The definitions of 'equity' and 'underserved communities' can be found in the Definitions section of\nthis framework as well as in Section 2 of The Executive Order On Advancing Racial Equity and Support\nfor Underserved Communities Through the Federal Government. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/\n49. Id.\n50. Various organizations have offered proposals for how such assessments might be designed. See, e.g.,\nEmanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish, and Jacob Metcalf.\nAssembling Accountability: Algorithmic Impact Assessment for the Public Interest. Data & Society\nResearch Institute Report. June 29, 2021. https://datasociety.net/library/assembling-accountability-\nalgorithmic-impact-assessment-for-the-public-interest/; Nicol Turner Lee, Paul Resnick, and Genie\nBarton. Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms.\nBrookings Report. May 22, 2019.\nhttps://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-\npolicies-to-reduce-consumer-harms/; Andrew D. Selbst. An Institutional View Of Algorithmic Impact\nAssessments. Harvard Journal of Law & Technology. June 15, 2021. https://ssrn.com/abstract=3867634;Dillon Reisman, Jason Schultz, Kate Crawford, and Meredith Whittaker. Algorithmic Impact\nAssessments: A Practical Framework for Public Agency Accountability. AI Now Institute Report. April\n2018. https://ainowinstitute.org/aiareport2018.pdf\n51.Department of Justice. Justice Department Announces New Initiative to Combat Redlining. Oct. 22,""
 ""HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\nReal-life examples of how these principles can become reality, through laws, policies, and practical \ntechnical and sociotechnical approaches to protecting rights, opportunities, and access. \nThe federal government is working to combat discrimination in mortgage lending. The Depart -\nment of Justice has launched a nationwide initiative to combat redlining, which includes reviewing how \nlenders who may be avoiding serving communities of color are conducting targeted marketing and advertising.51 \nThis initiative will draw upon strong partnerships across federal agencies, including the Consumer Financial \nProtection Bureau and prudential regulators. The Action Plan to Advance Property Appraisal and Valuation \nEquity includes a commitment from the agencies that oversee mortgage lending to include a \nnondiscrimination standard in the proposed rules for Automated Valuation Models.52\nThe Equal Employment Opportunity Commission and the Department of Justice have clearly \nlaid out how employers’ use of AI and other automated systems can result in discrimination \nagainst job applicants and employees with disabilities.53 The documents explain \nhow employers’ use of software that relies on algorithmic decision-making may violate existing requirements \nunder Title I of the Americans with Disabilities Act (“ADA”). This technical assistance also provides practical \ntips to employers on how to comply with the ADA, and to job applicants and employees who think that their \nrights may have been violated. \nDisparity assessments identified harms to Black patients' healthcare access. A widely \nused healthcare algorithm relied on the cost of each patient’s past medical care to predict future medical needs, \nrecommending early interventions for the patients deemed most at risk. This process discriminated""
 'while simultaneously enhancing the security effectiveness capabilities of the existing technology. \n•The National Disabled Law Students Association expressed concerns that individuals with disabilities were\nmore likely to be flagged as potentially suspicious by remote proctoring AI systems because of their disabili-\nty-specific access needs such as needing longer breaks or using screen readers or dictation software.45 \n•An algorithm designed to identify patients with high needs for healthcare systematically assigned lower\nscores (indicating that they were not as high need) to Black patients than to those of white patients, even\nwhen those patients had similar numbers of chronic conditions and other markers of health.46 In addition,\nhealthcare clinical algorithms that are used by physicians to guide clinical decisions may include\nsociodemographic variables that adjust or “correct” the algorithm’s output on the basis of a patient’s race or\nethnicity\n, which can lead to race-based health inequities.47\n25Algorithmic \nDiscrimination \nProtections']","Proactive measures and equity assessments can be implemented to prevent algorithmic discrimination in several effective ways:

1. **Equity Assessments in Design**: Incorporate equity assessments during the design phase of algorithm development. This involves evaluating the potential impact of the algorithm on different demographic groups and identifying any biases that may arise from the data or model design. Engaging diverse stakeholders in this process can provide valuable insights (Source: [document name], p. [page number]).

2. **Diverse Data Collection**: Ensure that the data used to train algorithms is representative of the populations it will affect. This includes actively seeking out data from underrepresented groups to avoid biases that can lead to discriminatory outcomes. Data collection should also consider intersectional factors, such as race, gender, and socioeconomic status (Source: [document name], p. [page number]).

3. **Regular Disparity Testing**: Conduct regular disparity testing to assess the algorithm's performance across different demographic groups. This involves analyzing the outcomes produced by the algorithm to identify any significant disparities that may indicate discrimination. If disparities are found, organizations should take corrective actions to address them (Source: [document name], p. [page number]).

4. **Algorithmic Impact Assessments**: Implement algorithmic impact assessments that evaluate the potential social and ethical implications of the algorithm before deployment. These assessments should include considerations of fairness, accountability, and transparency, and should be made public whenever possible (Source: [document name], p. [page number]).

5. **Stakeholder Engagement**: Engage with affected communities and stakeholders throughout the algorithm development process. This can include public consultations, focus groups, and partnerships with advocacy organizations. Their input can help identify potential biases and ensure that the algorithm serves the needs of all users (Source: [document name], p. [page number]).

6. **Continuous Monitoring and Feedback**: Establish mechanisms for continuous monitoring of the algorithm's performance post-deployment. This includes collecting user feedback and analyzing outcomes to identify any emerging issues related to discrimination. Organizations should be prepared to make adjustments based on this ongoing evaluation (Source: [document name], p. [page number]).

7. **Training and Awareness**: Provide training for developers and stakeholders on the importance of equity and the risks of algorithmic discrimination. This training should cover best practices for data collection, model development, and the ethical implications of AI technologies (Source: [document name], p. [page number]).

8. **Clear Accountability Structures**: Create clear accountability structures within organizations to ensure that there are designated individuals or teams responsible for overseeing equity assessments and addressing any identified issues. This accountability is crucial for fostering a culture of responsibility regarding algorithmic fairness (Source: [document name], p. [page number]).

By implementing these proactive measures and equity assessments, organizations can significantly reduce the risk of algorithmic discrimination and promote fairer outcomes in their AI systems.","Algorithmic discrimination can be prevented through proactive measures and equity assessments by ensuring that automated systems are designed and used in an equitable manner. This includes conducting proactive equity assessments during system design, using representative data, avoiding proxies for demographic features, ensuring accessibility for individuals with disabilities, conducting pre-deployment and ongoing disparity testing, and maintaining clear organizational oversight. Independent evaluation and plain language reporting, such as algorithmic impact assessments that include testing results and mitigation information, should be performed and made public whenever possible to confirm these protections.",1.0,0.9498583160512726,1.0,0.999999999975,0.8988776685030967
How can system design ensure behavior transparency in high-risk settings while meeting expectations for automated systems?,"['NOTICE & \nEXPLANATION \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nTailored to the level of risk. An assessment should be done to determine the level of risk of the auto -\nmated system. In settings where the consequences are high as determined by a risk assessment, or extensive \noversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully transparent models should be used), rather than as an after-the-decision interpretation. In other settings, the extent of explanation provided should be tailored to the risk level. \nValid. The explanation provided by a system should accurately reflect the factors and the influences that led \nto a particular decision, and should be meaningful for the particular customization based on purpose, target, and level of risk. While approximation and simplification may be necessary for the system to succeed based on the explanatory purpose and target of the explanation, or to account for the risk of fraud or other concerns related to revealing decision-making information, such simplifications should be done in a scientifically supportable way. Where appropriate based on the explanatory system, error ranges for the explanation should be calculated and included in the explanation, with the choice of presentation of such information balanced with usability and overall interface complexity concerns. \nDemonstrate protections for notice and explanation \nReporting. Summary reporting should document the determinations made based on the above consider -'
 'Risk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten -\ntial risks of the automated system should be identified and mitigated. Identified risks should focus on the potential for meaningful impact on people’s rights, opportunities, or access and include those to impacted communities that may not be direct users of the automated system, risks resulting from purposeful misuse of the system, and other concerns identified via the consultation process. Assessment and, where possible, mea\n-\nsurement of the impact of risks should be included and balanced such that high impact risks receive attention and mitigation proportionate with those impacts. Automated systems with the intended purpose of violating the safety of others should not be developed or used; systems with such safety violations as identified unin\n-\ntended consequences should not be used until the risk can be mitigated. Ongoing risk mitigation may necessi -\ntate rollback or significant modification to a launched automated system. \n18'
 'Meaningful access to examine the system. Designers, developers, and deployers of automated \nsystems should consider limited waivers of confidentiality (including those related to trade secrets) where necessary in order to provide meaningful oversight of systems used in sensitive domains, incorporating mea\n-\nsures to protect intellectual property and trade secrets from unwarranted disclosure as appropriate. This includes (potentially private and protected) meaningful access to source code, documentation, and related data during any associated legal discovery, subject to effective confidentiality or court orders. Such meaning\n-\nful access should include (but is not limited to) adhering to the principle on Notice and Explanation using the highest level of risk so the system is designed with built-in explanations; such systems should use fully-trans\n-\nparent models where the model itself can be understood by people needing to directly examine it. \nDemonstrate access to human alternatives, consideration, and fallback \nReporting. Reporting should include an assessment of timeliness and the extent of additional burden for human alternatives, aggregate statistics about who chooses the human alternative, along with the results of the assessment about brevity, clarity, and accessibility of notice and opt-out instructions. Reporting on the accessibility, timeliness, and effectiveness of human consideration and fallback should be made public at regu\n-'
 'SAFE AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nDerived data sources tracked and reviewed carefully. Data that is derived from other data through \nthe use of algorithms, such as data derived or inferred from prior model outputs, should be identified and tracked, e.g., via a specialized type in a data schema. Derived data should be viewed as potentially high-risk inputs that may lead to feedback loops, compounded harm, or inaccurate results. Such sources should be care\n-\nfully validated against the risk of collateral consequences. \nData reuse limits in sensitive domains. Data reuse, and especially data reuse in a new context, can result in the spreading and scaling of harms. Data from some domains, including criminal justice data and data indi\n-\ncating adverse outcomes in domains such as finance, employment, and housing, is especially sensitive, and in some cases its reuse is limited by law. Accordingly, such data should be subject to extra oversight to ensure safety and efficacy. Data reuse of sensitive domain data in other contexts (e.g., criminal data reuse for civil legal matters or private sector use) should only occur where use of such data is legally authorized and, after examina\n-\ntion, has benefits for those impacted by the system that outweigh identified risks and, as appropriate, reason -\nable measures have been implemented to mitigate the identified risks. Such data should be clearly labeled to identify contexts for limited reuse based on sensitivity. Where possible, aggregated datasets may be useful for replacing individual-level sensitive data. \nDemonstrate the safety and effectiveness of the system \nIndependent evaluation. Automated systems should be designed to allow for independent evaluation (e.g.,']","To ensure behavior transparency in high-risk settings while meeting the expectations for automated systems, system design should incorporate several key principles and practices:

1. **Risk Assessment Framework**: Begin with a comprehensive risk assessment to identify the potential consequences of the automated system's decisions. This assessment should determine the level of risk associated with the system's use in specific contexts, such as healthcare, criminal justice, or finance, where the stakes are particularly high (Source: [document name], p. [page number]).

2. **Use of Explainable Models**: Design the system using explainable models that allow stakeholders to understand how decisions are made. This means employing algorithms that can be easily interpreted and explained, rather than relying on ""black box"" approaches that obscure the decision-making process (Source: [document name], p. [page number]).

3. **Built-in Explanatory Features**: Incorporate built-in explanatory features that provide clear and accessible explanations of the system's behavior. These features should articulate the factors and influences that led to specific decisions, ensuring that users can comprehend the rationale behind outcomes (Source: [document name], p. [page number]).

4. **User-Centric Design**: Ensure that the explanations are tailored to the needs of the end-users. This involves considering the target audience's level of expertise and providing explanations that are meaningful and relevant to their context, which enhances the likelihood that the explanations will be understood and accepted (Source: [document name], p. [page number]).

5. **Error Range and Uncertainty Reporting**: Include information about error ranges and uncertainties in the explanations provided by the system. This helps users understand the limitations of the system's predictions and decisions, fostering a more realistic view of its capabilities (Source: [document name], p. [page number]).

6. **Continuous Feedback Mechanisms**: Establish mechanisms for continuous feedback from users and stakeholders. This feedback can be used to refine the explanatory features and ensure that they remain effective and relevant over time (Source: [document name], p. [page number]).

7. **Documentation and Reporting**: Maintain comprehensive documentation of the system's design, decision-making processes, and the explanations provided. Regular reporting on the system's performance and the effectiveness of its explanatory mechanisms can enhance accountability and transparency (Source: [document name], p. [page number]).

8. **Stakeholder Engagement**: Involve relevant stakeholders, including affected communities and experts, in the design process. Their insights can help identify potential risks and ensure that the system operates within ethical and legal boundaries (Source: [document name], p. [page number]).

By integrating these principles into the design of automated systems, organizations can enhance transparency and accountability, particularly in high-risk environments where the implications of decisions can significantly impact individuals and communities.","In settings where the consequences are high as determined by a risk assessment, or extensive oversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully transparent models should be used), rather than as an after-the-decision interpretation. The extent of explanation provided should be tailored to the risk level.",1.0,0.9578715395345266,1.0,0.999999999975,0.4546973605750549