An evidence-based methodology for human rights impact assessment (HRIA) in the development of AI data-intensive systems
<|reference_start|>An evidence-based methodology for human rights impact assessment (HRIA) in the development of AI data-intensive systems: Different approaches have been adopted in addressing the challenges of Artificial Intelligence (AI), some centred on personal data and others on ethics, respectively narrowing and broadening the scope of AI regulation. This contribution aims to demonstrate that a third way is possible, starting from the acknowledgement of the role that human rights can play in regulating the impact of data-intensive systems. The focus on human rights is neither a paradigm shift nor a mere theoretical exercise. Through the analysis of more than 700 decisions and documents of the data protection authorities of six countries, we show that human rights already underpin the decisions in the field of data use. Based on empirical analysis of this evidence, this work presents a methodology and a model for a Human Rights Impact Assessment (HRIA). The methodology and related assessment model are focused on AI applications, whose nature and scale require a proper contextualisation of HRIA methodology. Moreover, the proposed models provide a more measurable approach to risk assessment which is consistent with the regulatory proposals centred on risk thresholds. The proposed methodology is tested in concrete case-studies to prove its feasibility and effectiveness. The overall goal is to respond to the growing interest in HRIA, moving from a mere theoretical debate to a concrete and context-specific implementation in the field of data-intensive applications based on AI.<|reference_end|>
@article{mantelero2024an,
    title={An evidence-based methodology for human rights impact assessment (HRIA)
  in the development of AI data-intensive systems},
    author={Alessandro Mantelero and Maria Samantha Esposito},
    journal={2021. 41 Computer Law & Security Review 105561},
    year={2024},
    doi={10.1016/j.clsr.2021.105561},
    archivePrefix={arXiv},
    eprint={2407.20951},
    primaryClass={cs.AI}
}