Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?
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Abstract (arXiv)
Organisations can use artificial intelligence to make decisions about people for a variety of reasons, for instance, to select the best candidates from many job applications. However, AI systems can have discriminatory effects when used for decision-making. To illustrate, an AI system could reject applications of people with a certain ethnicity, while the organisation did not plan such ethnicity discrimination. But in Europe, an organisation runs into a problem when it wants to assess whether its AI system accidentally discriminates based on ethnicity: the organisation may not know the applicants' ethnicity. In principle, the GDPR bans the use of certain 'special categories of data' (sometimes called 'sensitive data'), which include data on ethnicity, religion, and sexual preference. The proposal for an AI Act of the European Commission includes a provision that would enable organisations to use special categories of data for auditing their AI systems. This paper asks whether the GDPR's rules on special categories of personal data hinder the prevention of AI-driven discrimination. We argue that the GDPR does prohibit such use of special category data in many circumstances. We also map out the arguments for and against creating an exception to the GDPR's ban on using special categories of personal data, to enable preventing discrimination by AI systems. The paper discusses European law, but the paper can be relevant outside Europe too, as many policymakers in the world grapple with the tension between privacy and non-discrimination policy.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2022.
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Citing papers per year: 2023: 4; 2024: 1; 2025: 2.
7 papers.
- 2023Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Surveycited by 30
- 2023The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucinationcited by 23
- 2023Multi-dimensional discrimination in Law and Machine Learning -- A comparative overviewcited by 15
- 2024Lazy Data Practices Harm Fairness Researchcited by 10
- 2023Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Developmentcited by 6
- 2025Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Toolcited by 1
- 2025Proportional Selection in Networkscited by 1
- 2025Proportional Selection in Networks5 Feb 2025
- 2025Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool3 Feb 2025
- 2024Lazy Data Practices Harm Fairness Research26 Apr 2024
- 2023Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey25 Sep 2023
- 2023Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development21 Jul 2023
- 2023The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucination21 Apr 2023
- 2023Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview12 Feb 2023