mlsanityData to 7 Oct 2026

Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness

arXiv 2403.20089 ↗First posted 29 Mar 2024cs.AIcited by 5

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Abstract (arXiv)

The topic of fairness in AI, as debated in the FATE (Fairness, Accountability, Transparency, and Ethics in AI) communities, has sparked meaningful discussions in the past years. However, from a legal perspective, particularly from the perspective of European Union law, many open questions remain. Whereas algorithmic fairness aims to mitigate structural inequalities at design-level, European non-discrimination law is tailored to individual cases of discrimination after an AI model has been deployed. The AI Act might present a tremendous step towards bridging these two approaches by shifting non-discrimination responsibilities into the design stage of AI models. Based on an integrative reading of the AI Act, we comment on legal as well as technical enforcement problems and propose practical implications on bias detection and bias correction in order to specify and comply with specific technical requirements.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 47 references that are in the map. First posted on arXiv 29 Mar 2024.

Builds on

19 of 47 references.

References · 19

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  2. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  3. 2017Counterfactual Fairnesscited by 802
  4. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  5. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 251
  6. 2018Women also Snowboard: Overcoming Bias in Captioning Modelscited by 236
  7. 2017No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing Worldcited by 152
  8. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 148
  9. 2018The Frontiers of Fairness in Machine Learningcited by 139
  10. 2019Principles alone cannot guarantee ethical AIcited by 134
  11. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  12. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
7 more
  1. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  2. 2022Fairness Testing: A Comprehensive Survey and Analysis of Trendscited by 61
  3. 2021Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloudcited by 19
  4. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  5. 2023Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Treecited by 14
  6. 2023Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparitycited by 5
  7. 2021Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions (preprint)cited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Robustness and Cybersecurity in the EU Artificial Intelligence Actcited by 10
  2. 2025Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyondcited by 2
  3. 2025Testing software for non-discrimination: an updated and extended audit in the Italian car insurance domaincited by 0
  4. 2025A Multi-Level Strategy for Deepfake Content Moderation under EU Regulationcited by 0
  5. 2026Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systemscited by 0