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

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arxiv 2403.20089 v2 pith:G5VT5OLS submitted 2024-03-29 cs.AI

classification cs.AI
keywords fairnessnon-discriminationalgorithmicbiaseuropeanimplicationslegalperspective
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Label bias and proxy features drive algorithmic unfairness more than underrepresentation of protected groups in training data, and a new Data Bias Profile quantifies these risks.

  2. Testing software for non-discrimination: an updated and extended audit in the Italian car insurance domain

    cs.SE 2025-02 conditional novelty 4.0 of 10

    A 2024 black-box audit of an Italian car insurance comparator confirms that birthplace, age, city, education, and other personal attributes still influence quoted premiums, and that some companies vary quote availabil...

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