The International Observatory on Vulnerable People In data protection
Resources
Disclaimer: The resources are arranged per thematic sections to facilitate the consultation. However, we are aware of the intersections and overlaps thereof. And that AI is generating new categories outside the traditional ones. We are also aware that pre-identifying categories of vulnerable people in data processing is not possible, vulnerability being a largely contextual and elusive concept. We are not claiming to be exhaustive, but to initiate a discussion.
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Non-discrimination, AI and data protection
- Academic writings
Caplan, R. et al. (2018). Algorithmic Accountability: A Primer. Data & Society report. https://datasociety.net/library/algorithmic-accountability-a-primer/
Costa, R. S., & Kremer, B. (2022). Inteligência artificial e discriminação: desafios e perspectivas para a proteção de grupos vulneráveis frente às tecnologias de reconhecimento facial. Revista Brasileira De Direitos Fundamentais & Justiça, 16(1). https://doi.org/10.30899/dfj.v16i1.1316
Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press https://yalebooks.yale.edu/book/9780300264630/atlas-of-ai/
Kamiran, F., Calders, T., & Pechenizkiy, M. (2013). Techniques for Discrimination-Free Predictive Models, in Custers et al. (eds) Discrimination and Privacy in the Information Society, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-30487-3_12
Kasy, M., & Abebe, R. (2021, March). Fairness, equality, and power in algorithmic decision-making. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 576-586. https://doi.org/10.1145/3442188.3445919
Krupiy, Tetyana. “Why the Proposed Artificial Intelligence Regulation Does Not Deliver on the Promise to Protect Individuals from Harm.” European Law Blog, 2021. https://europeanlawblog.eu/2021/07/23/why-the-proposed-artificial-intelligence-regulation-does-not-deliver-on-the-promise-to-protect-individuals-from-harm/
Krupiy, Tetyana (Tanya). “A Vulnerability Analysis: Theorising the Impact of Artificial Intelligence Decision-Making Processes on Individuals, Society and Human Diversity from a Social Justice Perspective.” Computer Law and Security Review 38 (2020): 105429. https://doi.org/10.1016/j.clsr.2020.105429
Leavy, S. (2018). Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning,” 2018 IEEE/ACM 1st International Workshop on Gender Equality in Software Engineering (GE), 14-16.
Mesch, G. S., & Dodel, M. (2018). Low self-control, information disclosure, and the risk of online fraud. American Behavioral Scientist, 62(10), 1356-1371 https://doi.org/10.1177/0002764218787854
Mullaney et al. (eds.) (2021). Your Computer Is on Fire. MIT Press. https://direct.mit.edu/books/book/5044/Your-Computer-Is-on-Fire
Patton, D. U., Brunton, D. W., Dixon, A., Miller, R. J., Leonard, P., & Hackman, R. (2017). Stop and frisk online: Theorizing everyday racism in digital policing in the use of social media for identification of criminal conduct and associations. Social Media+ Society, 3(3). https://doi.org/10.1177/2056305117733344
Tzanou, Maria. “The Future of Eu Data Privacy Law: Towards a More Egalitarian Data Privacy.” Journal of International and Comparative Law 7, no. 2 (2020): 449–70. https://ssrn.com/abstract=3710528
Van Bekkum, M. and Zuiderveen Borgesius, F. (2022). Using sensitive data to prevent discrimination by artificial intelligence: does the GDPR need a new exception?. Available at SSRN: https://ssrn.com/abstract=4104823 or http://dx.doi.org/10.2139/ssrn.4104823
- NGO Reports & Articles
- Data Protection Authorities' Guidance
- Laws
- Case Law
- European Court of Human Rights
- Court of Justice of the European Union
Heinz Huber v. Bundesrepublik Deutschland (Grand Chamber) ECLI:EU:C:2008:724 https://curia.europa.eu/juris/document/document.jsf;jsessionid=8DD1C951C52E367EC885CE5EC4916346?text=&docid=76077&pageIndex=0&doclang=en&mode=lst&dir=&occ=first&part=1&cid=897854