Privacy plays a crucial role in preserving democratic ideals and personal autonomy. The dominant legal approach to privacy in many jurisdictions is the Notice and Choice paradigm, where privacy policies are the primary instrument used to convey information to users. However, privacy policies are long and complex documents that are difficult for users to read and comprehend. We discuss how language technologies can play an important role in addressing this information gap, reporting on initial progress towards helping three specific categories of stakeholders take advantage of digital privacy policies: consumers, enterprises, and regulators. Our goal is to provide a roadmap for the development and use of language technologies to empower users to reclaim control over their privacy, limit privacy harms, and rally research efforts from the community towards addressing an issue with large social impact. We highlight many remaining opportunities to develop language technologies that are more precise or nuanced in the way in which they use the text of privacy policies. Privacy is a fundamental right central to a democratic society, in which individuals can operate as autonomous beings free from undue interference from other individuals or entities (Assembly, 1948). However, certain functions of privacy, such as the power to grant or deny access to one's personal information, are eroded by modern commercial and business practices that involve vast collection, linking, sharing, and processing of digital personal information through an opaque network, often without data subjects' knowledge or consent. In many jurisdictions, online privacy is largely governed by Notice and Choice (Federal Trade Commission, 1998). Under this framework, data-collecting and data-processing entities publish privacy policies that disclose their data practices. Theoretically, users are free to make choices about which services and products they use based on the disclosures made in these policies. Thus, the legitimacy of this framework hinges on users reading a large number of privacy policies to understand what data can be collected and how that data can be processed before making informed privacy decisions. In practice, people seldom read privacy policies, as this would require prohibitive amounts of their time (McDonald and Cranor, 2008; Cate, 2010; Cranor, 2012; Reidenberg et al., 2015; Schaub et al., 2015; Jain et al., 2016). Thus, an opportunity exists for language technologies to bridge this gap by processing privacy policies to meet the needs of Internet and mobile users. NLP has made inroads in digesting large amounts of text in domains such as scientific publications and news (Jain et al., 2020; Cachola et al., 2020; Kang et al., 2018; Rush et al., 2015; See et al., 2017), with several practical tools based on these technologies helping users every day (Cachola et al., 2020; TLDR, 2021; News, 2021). These domains have also received considerable research attention: several benchmark datasets and technologies are based in texts from these domains (Nallapati et al., 2016; See et al., 2017; Narayan et al., 2018; Beltagy et al., 2019). We highlight that the privacy domain can also benefit from increased research attention from the community. Moreover, technologies developed in the privacy domain have potential for significant and large-scale positive social impactthe affected population includes virtually every Internet or mobile user (Sadeh et al., 2013). Automated processing of privacy policies opens the door to a number of scenarios where language technologies can be developed to support users in the context of different tasks. This includes saving data subjects the trouble of having to read the entire text of policies when they are typically only concerned about one or a small number of issues (e.g., determining whether they can opt out of some practices or whether some of their data might be shared with third parties). It includes helping companies ensure that they are compliant and that their privacy policies are consistent with what their code actually does. It also includes supporting regulators, as they face the daunting task of enforcing compliance across an ever-growing collection of software products and processes, including sophisticated data collection and use practices. In this work, we conduct an extensive survey of initial progress in applying NLP to address limitations of the Notice and Choice model. We expect our work to serve as a useful starting point for practitioners to familiarize themselves with technological progress in this domain, by providing both an introduction to the basic privacy concerns and frameworks surrounding privacy policies, as well as an account of applications for which language technologies have been developed. Finally, we highlight many remaining opportunities for NLP technologies to extract more precise, more nuanced, and ultimately more useful information from privacy policy text describing key challenges in this area and laying out a vision for the future. In 1890, Warren and Brandeis defined the right to privacy as the right to be let alone(Warren and Brandeis, 1890). More recently, Westin defined the right as the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others (Westin, 1968). A primary aspiration of privacy is to allow for the separation of individual and society as a means of fostering personal autonomy. To that end, privacy protects the situated practices of boundary management through which the capacity for self-determination develops, and further shelters dynamic, emergent subjectivity from the efforts of commercial and government actors to render individuals and communities fixed, transparent, and predictable (Cohen, 2012). Privacy, therefore, is foundational to the practice of informed and reflective citizenship, and serves as an indispensable structural feature of liberal democratic political systems (Cohen, 2012). When privacy is threatened, we risk losing the chance for critical self-reflection of political processes and social norms. Indeed, privacy undergirds the concepts of human dignity and other key values, such as the freedoms of association and speech. For these reasons and others, privacy is regarded as a fundamental human right (Assem-bly, 1948). In the digital age, privacy is threatened by aggressive, rapid, and largely automated collection, linking, sharing, and processing of digital personal information. Digital privacy is intrinsically linked to the fundamental ethical principles of transparency , fairness and agency . Transparency : Users have a right to know how information about them is collected and used. Entities collecting user data stay clear of manipulative schemes designed to influence the data subject's willingness to disclose their data (e.g. overemphasizing benefits while remaining silent about potential risks associated with the disclosure of data in a given context). Fairness : Users should receive perceived value commensurate to the perceived loss of privacy associated with disclosure and use of their data. Agency : Users should have a choice about what data is collected about them and how it is used. The dominant paradigm to address these principles in the United States and most legal jurisdictions around the world, is the 'Notice and Choice' regulatory framework (Westin, 1968; Federal Trade Commission, 1998). 'Notice and Choice' regimes are based on the presupposition that consumers will adequately manage their privacy, if provided sufficient information about how their data will be collected, used and managed, as well as offered meaningful choices. Today, 'Notice' is often practically realized through publishing privacy policies , which are long and verbose documents that users are expected to read and understand. Choice' is often limited to the user clicking I agree' to the privacy policy, or even interpreting their continued use of the service as some sort of meaningful consent to the terms of the policy. The 'Notice and Choice' framework is fundamentally broken. In practice, users seldom read privacy policies (McDonald and Cranor, 2008; Cate, 2010; US Federal Trade Commission et al., 2012) and it is prohibitively expensive for them to even do so. McDonald and Cranor (2008) estimate that if internet users were to actually read the privacy policies of the websites they visited, they would have to spend roughly 250 hours each year just reading Challenge Example Ambiguity We may also use aggregate personal information for regulatory compliance, industry and market analysis, research, demographic profiling, marketing and advertising, and other business purposes. privacy policies. A 2014 report from the Presidents Council of Advisors on Science and Technology stated that only in some fantasy world were users reading and understanding privacy policies before giving their consent (of the President's Council of Advisors on Science and Technology, 2014). Indeed, 91% of people in the U.S have reported feeling like they have lost control over their information (Madden et al., 2014). Moreover, recent privacy laws such as the EU's General Data Protection Regulation (GDPR) (Regulation, 2016) still fail to address the critical limitation of notice and choice: the continued reliance on users to read and understand a large number of privacy policies. Studies have shown that GDPR requirements have actually resulted in longer privacy policies (Linden et al., 2020), and users still encounter unreadable privacy policies (Becher and Benoliel, 2019). The lack of respect for individuals' rights to privacy also has implications for society. With social platforms in particular having access to an unprecedented scale of information about human behaviour, Vicario et al. (2019) discuss that users' polarization and confirmation bias can play a role in spreading misinformation on social platforms. Madden et al. (2017) report that particular groups of less-privileged users on the internet are uniquely vulnerable to various forms of surveillance and privacy harms, which could widen existing economic gaps. Introna (1997) describe privacy as central to human autonomy in social relationships. In this work, we examine the potential of language technologies in enabling people to derive the benefits of their rights to transparency, fairness and agency. Privacy policies present interesting challenges for NLP practitioners, as they often feature characteristic aspects of language that remain under-examined or difficult to process (Table. 1). For example, while many policies discuss similar issues surrounding how user data is collected, managed and stored, policy silence about certain data practices may carry great weight from a legal, policy, and regulatory perspective. 1 In the privacy policy domain, understanding what has not been said in a privacy policy ( policy silence ) is just as important as understanding what is said (Zimmeck et al., 2019a; Marotta-Wurgler, 2019). Further, though policies tend to feature literal language (compared to more subjective domains like literature or blog posts), processing them ef-1 For example, in United States v. Path , the defendant's (Path) privacy policy described that its app collected certain information such as your Internet Protocol (IP) address, your operating system, the browser type. The Federal Trade Commission found this disclosure to be incomplete and insufficient to provide notice about the collection of users' contact data (FTC, 2013). fectively also requires several additional capabilities such as reasoning over vagueness and ambiguity, understanding elements such as lists (in-cluding when they are intended to be exhaustive and when they are not (Bhatia et al., 2016)), effectively incorporating co-text'aspects of web document structure such as document headers that are meaningful semantically to the content of privacy policies(Mysore Gopinath et al., 2018) and incorporating domain knowledge (for example, understanding whether information is sensitive requires background knowledge in the form of applicable regulation). Privacy policies also differ from several closely related domains, such as legal texts which are largely meant to be processed by domain experts. In contrast, privacy policies are legal documents with legal effectsgenerally drafted by expertsthat are ostensibly meant to be understood by everyday users. NLP applications in the privacy domain also need to be designed with end user requirements in mind. For example, from a legal standpoint, when generating answers to a user's question about the content of a privacy policy, it is generally advisable to include disclaimers, but users may prefer to be presented with shorter answers, where disclaimers are kept as short as possible. Challenges are described in more detail in (4). We survey current efforts to apply NLP in the privacy domain, discussing both existing task formulations as well as future areas in this domain where language technologies can have impact. 2 2 Our survey includes relevant papers from major NLP venues, including ACL, EMNLP, NAACL, EACL, COLING, CoNLL, SemEval, TACL, and CL. We supplemented these publications with a review of the literature at venues such as SOUPS, PETS, WWW, ACM, and NDSS. We also included relevant legal venues, such as law reviews and journals. Initial efforts in applying NLP in the privacy domain have largely focused on discovering or identifying data practice categories in privacy policies (Costante et al., 2012a; Ammar et al., 2012; Costante et al., 2012b; Liu et al., 2014b; Ramanath et al., 2014a; Wilson et al., 2016b). Automating the identification of such data practices could potentially support users in navigating privacy policies more effectively 3 , as well as automate analysis for regulators who currently do not have techniques to assess a large number of privacy policies. Wilson et al. (2016b) create a corpus of 115 website privacy policies annotated with detailed information of the privacy policies described. The corpus and associated taxonomy have been of utility in the development of several subsequent privacy-enhancing language technologies (Mysore Sathyendra et al., 2017a; Zimmeck et al., 2017; Ravichander et al., 2019; Ahmad et al., 2020). Studies have shown that consumers desire control over the use of their information for marketing communication, and object to the use of their information for web tracking or marketing purposes including targeted advertising (Cranor et al., 2000; Turow et al., 2009; Ur et al., 2012; Bleier and Eisenbeiss, 2015). However, McDonald and Cranor (2010) find that many people are unaware of the opt-out choices available to them. These choices are often buried in policy text, and thus there has been interest in applying NLP to extract choice language. Mysore Sathyendra et al. (2017b) automatically identify choice instances within a privacy 3 For example, through the data exploration tool developed by the Usable Privacy Policy Project: https://explore. usableprivacy.org/?view=machine Figure 1: The results from Opt-Out Easy, a browser extension to extract opt-out choices from privacy policies, for Overleaf.com (Bannihatti Kumar et al., 2020). policy, labeling different types of opt-out choices, with a particular emphasis on extracting actionable choices in the policy, i.e. those associated with hy-perlinks. Bannihatti Kumar et al. (2020) develop a web-browser extension to present extracted choice instances to users (Figure. 1), finding that the tool can considerably increase awareness of choices available to users and reduce the time taken to identify actions the users can take. In 2012, six major mobile app stores entered into an agreement with the California Attorney General, where they agreed to adopt privacy principles that require mobile apps to have privacy poli-cies(Justice, 2012). Regulations such as the the EU General Data Protection Directive (GDPR) and the California Consumer Protection Act (CCPA) impose further requirements on what entities collecting and using personal data need to disclose in their privacy policies and what rights they need to offer to their users (e.g. privacy controls, option to request deletion of one's data). However, regulators lack the necessary resources to systematically check that these requirements are satisfied. In fact, even app stores lack the resources to systematically check that disclosures made in privacy policies are consistent with the code of apps and comply with relevant regulatory requirements. Thus, there has been interest in developing technologies to automatically identify potential compliance issues (Enck et al., 2014; Zimmeck et al., 2017; Wang et al., 2018; Libert, 2018a; Zimmeck et al., 2019b). aid compliance analysis is detailed by Zimmeck et al. (2017), including results of a systematic analysis of 17,991 apps using both natural language processing and code analysis techniques. Classi-fiers are trained to identify data practices based on the OPP-115 ontology (Wilson et al., 2016b), and static code analysis techniques are employed to extract app's privacy behaviors. The results from the two procedures are compared to identify potential compliance issues. The system was piloted with personnel at the California Office of the Attorney General. Users reported that the system could sig-nificantly increase productivity, and decrease the effort and time required to analyze practices in apps and audit compliance. Zimmeck et al. (2019b) review 1,035,853 apps from the Google Play Store for compliance issues. Their system identifies disclosed privacy practices in policies using classi-fiers trained on the APP-350 corpus (Story et al., 2019), and static code analysis techniques to identify apps' privacy behaviors. Results of the analysis of this large corpus of privacy policies revealed a particularly large number of potential compliance problems, with a subset of results shared with the Federal Trade Commission. The system was also reported to have been used by a large electronics manufacturer to verify compliance of legacy mobile apps prior to the introduction of GDPR. Due to the lengthy and verbose nature of privacy policies, it is appealing to attempt to develop automated text summarization techniques to generate short and concise summaries of a privacy policy's contents (Liu et al., 2015). Tomuro et al. (2016) develop an extractive summarization system that identifies important sentences in a privacy policy along five categories: purpose, third parties, limited collection, limited use and data retention. Zaeem et al. (2018, 2020) identify ten questions about privacy policies, and automatically categorize risk levels' associated with each of the questions, as shown in Table. 3. Keymanesh et al. (2020) focus on extractive summarization approaches to identify risky sections' of the privacy policy, which are sentences that are likely to describe a privacy risk posed to the end-user. However, while automated summarization seems like a promising application of language technologies, identifying which parts of a policy should be shown to users is exceedingly difficult, and studies by privacy experts have shown # Question Green Risk Level Yellow Risk Level Red Risk Level (1) How well does this website protect your email address? that such one-size-fits-all' approaches are unlikely to be effective (Gluck et al., 2016; Rao et al., 2016). A desire to move away from one-size-fits-all' approaches has led to increased interest in supporting automated privacy question-answering (QA) capabilities. If realized, such functionality will help users selectively and iteratively explore issues that matter most to them. Table 4 lists current efforts to develop resources for privacy question-answering. Amongst the initial explorations in this area, Hark-ous et al. (2018) examine privacy questions asked by Twitter users to companies, with answers annotated by the paper's authors. Ravichander et al. (2019) collect questions asked by crowdworkers about a mobile app without seeing the app's privacy policy, and hire legal experts to identify sentences in the privacy policy relevant for each question. (Ahmad et al., 2020) provide skilled anno-tators' with privacy policy segments drawn from the OPP-115 corpus (Wilson et al., 2016b), and ask them to construct questions based on the provided span of text. Ravichander et al. (2019) and Ahmad et al. (2020) both find that current QA baselines based on pretrained language models(Devlin et al., 2019) are inadequate for answering privacy questions. Ahmad et al. (2020) indicate that identifying longer evidence spans are challenging and describe transfer learning as a potential direction to improve performance. Ravichander et al. (2019) examine unanswerability as a challenge to privacy QA systems, highlighting the many facets of unanswerable questions that can be asked. It is worth noting that all three resources formulate ground truth based in the text of the privacy policy, but policy language is difficult for non-experts to understand (Reiden-berg et al., 2015). Future QA dataset architects could consider abstractive answers as ground truths, which are validated by legal experts for correctness and evaluated by users for helpfulness. It may also be desirable for benchmarks to aim for ecological validity (de Vries et al., 2020), with users asking questions, and legal experts constructing answers. In this section, we survey further tasks where NLP has been applied to consumer privacy, including analyzing privacy policy readability , with the goal of aiding writers of privacy policies (Fabian et al., 2017; Massey et al., 2013; Meiselwitz, 2013; Ermakova et al., 2015), and understanding data practice categories are described in a policy, known as measuring policy coverage (Lin-den et al., 2020; Shvartzshnaider et al., 2020). A significant amount of recent work has also focused on information extraction from privacy policies (Costante et al., 2012a). Shvartzshanider et al. (2018); Shvartzshnaider et al. (2019, 2020) identify contextual integrity parameters (Nissenbaum, 2004) in policy text. Studies have also tried to extract other, more specific kinds of information from policies, such as third party entities (Libert, 2018b; Bokaie Hosseini et al., 2020) and information about regulated information types (Bhatia et al., 2016; Evans et al., 2017) as well as their similarity (Hosseini et al., 2016). There have also been efforts to analyze vague statements in privacy policies (Liu et al., 2016b; Lebanoff and Liu, 2018), and explore how benchmarks in this domain can be constructed through crowdsourcing (Ramanath et al., 2014b; Wilson et al., 2016c; Audich et al., 2018). Lastly, there has been research focused on identifying header information in privacy policies (Mysore Gopinath et al., 2018) and generating them (Gopinath et al., 2020). Techniques to Dataset #Questions QuestionScenario Legal Expert Annotator Asker Cannot See Evidence UnanswerableQuestions Non-ContiguousAnswer Polisis (Harkous et al., 2018) 120 Twitter users ask questions to a company. process privacy policies have largely followed successful approaches elsewhere in NLP, starting from feature-based approaches (Sathyendra et al., 2017; Zimmeck et al., 2019a), training domain-specific word embeddings (Kumar et al., 2019) and fine-tuning pretrained language models on privacy policies (Nejad et al., 2020; Mustapha et al., 2020). We discuss a vision of future applications of NLP in aiding consumer privacy. We believe these applications present interesting opportunities for the community to develop technologies, both because of the technical challenges they offer and the im-pact they are likely to have. Detecting surprising statements: Since users do not read privacy policies, their expectations for the data practices of services might not align with services' actual practices. These mismatches may result in unexpected privacy risks which lead to loss of user trust (Rao et al., 2016). Identifying such surprising' statements will require understanding social context and domain knowledge of privacy information types. For example, it is natural for a banking website to collect payment information, but not health information. Moreover, understanding what statements will be surprising for each individual user requires understanding their personal, social and cultural backrounds (Rao et al., 2016). We speculate that NLP can potentially be leveraged to increase transparency by identifying discordant statements within privacy policies. Detecting missing information: In contrast to detecting surprising statements, privacy policies may be underspecified . Story et al. (2018) find that many policies contain language appearing in unrelated privacy policies, indicating that policy writers may use privacy policy generators not suited to their application, potentially resulting in missing information. Techniques from compliance analysis could help in flagging some of these issues (Zim-meck et al., 2017, 2019a). Generating privacy nutrition labels: One proposal to overcome the gap in communicating privacy information to users has been the privacy nutrition label' approach (Kelley et al., 2009, 2013), as shown in Fig. 2. The proposal draws from industries such as nutrition, warning and energy labeling where information has to be communicated to consumers in a standardized way. Recently, Apple announced that developers will be required to provide information for these labels (Campbell, 2020), which disclose to the user the information a company and third parties collect. 4 This approach could potentially be helpful to users to understand privacy information at a glance, but presents challenges to both developers and app platforms. Developers need to ensure their nutrition label is accurate and platforms need to enforce compliance to these requirements. Potentially, early successes of language technologies in compliance systems can be extended to analyzing a specified nutrition label, policy and application code. NLP may also be used to generate nutrition labels which developers inspect, as opposed to the more costly process of developers specifying nutrition labels from scratch which may hinder adoption (Fowler, 2021). Personalized privacy summaries: One approach to mitigating inadequacies of policy summarizationwhere generic summaries may not be sufficiently complete is personalized summarization (Daz and Gervas, 2007; Hu et al., 4 An example of such a nutrition label can be found in Appendix. A 2012). In this formulation, policies are summarized for each user based on issues that matter most to them. This formulation may alleviate some down-sides of QA approaches, which require the user know how to manage their privacy by asking the right questions. Personalized summarization systems would benefit from modeling users' level of knowledge, as well as their beliefs, desires and goals. In NLP, there has been effort towards addressing similar challenges for personalized learning in intelligent tutoring (McLaren et al., 2006; Malpani et al., 2011). Assistive Policy Writing: We speculate advances in natural language generation and compliance analysis techniques may jointly be leveraged to help app developers create more accurate privacy policies, rather than relying on policy generators (Story et al., 2018). Privacy policies generally cover a known set of data practices (Wilson et al., 2016a), providing potential statistical commonalities to aid natural language generation. Code analysis can be leveraged to constrain generation to accurately describe data practices of a service. Although privacy policies have legal effects for most Internet users, these types of texts constitute an underserved domain in NLP. NLP has the potential to play a role in easing user burden in understanding salient aspects of privacy policies, help regulators enforce compliance and help developers enhance the quality of privacy policies by reducing the effort required to construct them. Yet, the privacy domain presents several challenges that require specialized resources to deal with effectively. We describe some of these distinctive challenges, as well as the capabilities that will need to be developed to process policies satisfactorily. Disagreeable privacy policies: Privacy policies are complex, but are the most important source of information about how user data is collected, managed and used. Reidenberg et al. (2015) find that sometimes discrepancies can arise in the interpretation of policy language, even between experts. This additional complexity should be taken into consideration by those developing language technologies in this domain. Difficulty or validity of collecting annotations: Privacy policies are legal documents that have legal effects on how user data is collected and used. in this domain (Wilson et al., 2016c), individual practitioners constructing applications must carefully consider the consequences of sourcing non-expert annotations in the context of their task and the impacted stakeholders, and not rely on crowdsourced annotation simply because it is cheaper or easier to scale. Difficult for users to articulate their needs and questions: Developing effective privacy QA functionality will require understanding the kinds of questions users ask and quantifying to what extent privacy literacy affects users' ability to ask the right questions. Ravichander et al. (2019) find many questions collected from crowdworkers were either incomprehensible, irrelevant or atypical. Understanding these factors could lead to the development of more proactive QA functionalityfor example, rather than wait for users to form questions, the QA system could prompt users to reflect on certain privacy issues. Challenges to QA : Additionally, privacy question-answering systems themselves will require several capabilities in order to have larger impact. These systems must be capable of doing question-answering iteratively, working with the user towards resolving information-seeking needs. They will also need to consider unan-swerability(Rajpurkar et al., 2018; Ravichander et al., 2019; Asai and Choi, 2020) as a graded problem, recognizing to what extent the privacy policy contains an answer and communicating both what is known and what is not known to the user. QA systems must also consider what kinds of answers are useful, identifying appropriate response format and tailoring answers to the user's level of knowledge and individual preferences. Domain Knowledge : It remains an open question how to best incorporate expert knowledge into the processing of privacy policies. Although privacy policies are intended to be read by everyday users, experts and users often disagree on their interpretations (Reidenberg et al., 2015). Combining Disparate Sources of Information : While privacy policies are the single most important source of information about collection and sharing practices surrounding user data, technologies to address users' personalized concerns could leverage additional sources of information-such as analyzing the code of a given technology such as a mobile app, news articles, or background knowledge of a legal, technical or statistical nature. For example, when the policy is silent on an issuea QA system could report the practices of other similiar services to the user, or if a user asks about the likelihood of a data breach, the QA system could refer to news sources for information about the service. User Modeling : Personalized privacy approaches will also need to model individual user's personal, social and cultural contexts to deliver impact. This could include information about the issues likely to matter most to users, their background knowledge, privacy preferences and expectations (Liu et al., 2014a; Lin et al., 2014; Liu et al., 2016a). Accessibility: Efforts to help users understand privacy policies by breaking through walls of text to identify salient aspects, are expected to help users with a range of visual impairments navigate their privacy. Future work would conduct user studies to determine the extent to which developed technologies ease visually-impaired users' accessibility to learn about the content of policies, related to their interests or concerns. While NLP has the potential to benefit consumer privacy, we emphasize there are also ethical considerations to be taken in account. These include: Bias of agent providing technology: A factor that must be considered in the practical deployment of NLP systems in this domain is the incentives of the entity creating or providing the technology. For example, the incentives of a company that develops a QA system to answer questions about its own privacy policy may not align with those of a trusted third-party privacy assistant that reviews the privacy policies of many different companies. This information also needs to be communicated in an accurate and unbiased fashion to users. User Trust: While NLP systems have the potential to digest policy text and present information to users, NLP systems are seldom completely accurate, and therefore it is important that users be appropriately informed of these limitations. For example, if a QA system communicates a data practice incorrectly in response to a users' question and the user encounters privacy harms contrary to their expectations as a result, they may lose trust in the system. Discriminatory Outcomes: It is possible that different populations will benefit to different extents from the developed technologies, and we are yet unable to anticipate precisely where the benefits will accrue. For example, users with higher degrees of privacy literacy may be able to take better advantage of a developed QA system. Technological Solutionism: It is important to consider that while language technologies have the potential to considerably alleviate user burden in reading privacy policies, they are unlikely to completely resolve the issue that users are unable to read and review a multitude of privacy policies everyday. Advances toward addressing the limitations of notice and choice will also require progress in regulation and enforcement by regulatory bodies to ensure that enterprises are more accurate in their disclosures and use clearer language, in tandem with creative technological solutions. Privacy is about the right of people to control the collection and use of their data. Today privacy relies on the 'Notice and Choice' framework, which assumes that people actually read the text of privacy policies. This is a fantasy as users do not have the time to do so. In this article, we summarize how language technologies can help overcome this challenge and support the development of solutions that assist customers, technology providers and regulators. We reviewed early successes and presented a vision of how NLP could further help in the future. We hope this article will motivate NLP researchers to contribute to this vision and empower people to regain control over their privacy. This research was supported in part by grants from the National Science Foundation Secure and Trustworthy Computing program (CNS-1330596, CNS-1330214, CNS-15-13957, CNS-1801316, CNS-1914486, CNS-1914444) and DARPA(FA8750-15-2-0277). Part of the work summarized in this paper was conducted by the Usable Privacy Policy Project( https://usableprivacy.org ). The authors would like to thank Siddhant Arora, Rex Chen and Aakanksha Naik for valuable discussion.
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