mlsanityData to 14 Oct 2026

It's complicated. The relationship of algorithmic fairness and non-discrimination provisions for high-risk systems in the EU AI Act

arXiv 2501.12962 ↗First posted 22 Jan 2025cs.LG cs.AI cs.CYcited by 0

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Abstract (arXiv)

What constitutes a fair decision? This question is not only difficult for humans but becomes more challenging when Artificial Intelligence (AI) models are used. In light of discriminatory algorithmic behaviors, the EU has recently passed the AI Act, which mandates specific rules for high-risk systems, incorporating both traditional legal non-discrimination regulations and machine learning based algorithmic fairness concepts. This paper aims to bridge these two different concepts in the AI Act through: First, a necessary high-level introduction of both concepts targeting legal and computer science-oriented scholars, and second, an in-depth analysis of the AI Act's relationship between legal non-discrimination regulations and algorithmic fairness. Our analysis reveals three key findings: (1.) Most non-discrimination regulations target only high-risk AI systems. (2.) The regulation of high-risk systems encompasses both data input requirements and output monitoring, though these regulations are partly inconsistent and raise questions of computational feasibility. (3.) Finally, we consider the possible (future) interaction of classical EU non-discrimination law and the AI Act regulations. We recommend developing more specific auditing and testing methodologies for AI systems. This paper aims to serve as a foundation for future interdisciplinary collaboration between legal scholars and computer science-oriented machine learning researchers studying discrimination in AI systems.

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References · 29

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2018Datasheets for Datasetscited by 1,386
  3. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  4. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  5. 2023AI Alignment: A Comprehensive Surveycited by 263
  6. 2023Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMscited by 199
  7. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 149
  8. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  9. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 125
  10. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  11. 2018The Measure and Mismeasure of Fairnesscited by 95
  12. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
17 more
  1. 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
  2. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 58
  3. 2018A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycited by 53
  4. 2023The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by defaultcited by 36
  5. 2024COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Actcited by 24
  6. 2024Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecuritycited by 21
  7. 2024Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classificationcited by 18
  8. 2023Multi-dimensional discrimination in Law and Machine Learning -- A comparative overviewcited by 15
  9. 2023Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Treecited by 14
  10. 2023Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPcited by 11
  11. 2019Oxford Handbook on AI Ethics Book Chapter on Race and Gendercited by 7
  12. 2024Fairness in Large Language Models in Three Hourscited by 7
  13. 2024The Fundamental Rights Impact Assessment (FRIA) in the AI Act: Roots, legal obligations and key elements for a model templatecited by 7
  14. 2023Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithmscited by 5
  15. 2024Towards Effective Discrimination Testing for Generative AIcited by 5
  16. 2024Using sensitive data to de-bias AI systems: Article 10(5) of the EU AI Actcited by 3
  17. 2023Acceptable risks in Europe's proposed AI Act: Reasonableness and other principles for deciding how much risk management is enoughcited by 2

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