On Algorithmic Fairness and the EU Regulations

• URL: http://arxiv.org/abs/2411.08363v2
• Date: Sat, 21 Dec 2024 10:08:04 GMT
• Title: On Algorithmic Fairness and the EU Regulations
• Authors: Jukka Ruohonen,
• Abstract summary: The paper focuses on algorithmic fairness focusing on non-discrimination in the European Union (EU) The paper demonstrates that correcting discriminatory biases in AI systems can be legally done under the EU regulations. The paper contributes to the algorithmic fairness research with a few legal insights, enlarging and strengthening the growing research domain of compliance in AI engineering.
• Score: 0.2538209532048867
• License:
• Abstract: The short paper discusses algorithmic fairness by focusing on non-discrimination and a few important laws in the European Union (EU). In addition to the EU laws addressing discrimination explicitly, the discussion is based on the EU's recently enacted regulation for artificial intelligence (AI) and the older General Data Protection Regulation (GDPR). Through a theoretical scenario analysis, on one hand, the paper demonstrates that correcting discriminatory biases in AI systems can be legally done under the EU regulations. On the other hand, the scenarios also illustrate some practical scenarios from which legal non-compliance may follow. With these scenarios and the accompanying discussion, the paper contributes to the algorithmic fairness research with a few legal insights, enlarging and strengthening also the growing research domain of compliance in AI engineering.

Related papers

• The Fundamental Rights Impact Assessment (FRIA) in the AI Act: Roots, legal obligations and key elements for a model template [55.2480439325792]
  Article aims to fill existing gaps in the theoretical and methodological elaboration of the Fundamental Rights Impact Assessment (FRIA) This article outlines the main building blocks of a model template for the FRIA. It can serve as a blueprint for other national and international regulatory initiatives to ensure that AI is fully consistent with human rights.
  arXiv  Detail & Related papers  (2024-11-07T11:55:55Z)
• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• An evidence-based methodology for human rights impact assessment (HRIA) in the development of AI data-intensive systems [49.1574468325115]
  We show that human rights already underpin the decisions in the field of data use. This work presents a methodology and a model for a Human Rights Impact Assessment (HRIA) The proposed methodology is tested in concrete case-studies to prove its feasibility and effectiveness.
  arXiv  Detail & Related papers  (2024-07-30T16:27:52Z)
• Formalising Anti-Discrimination Law in Automated Decision Systems [1.560976479364936]
  We introduce a novel decision-theoretic framework grounded in anti-discrimination law of the United Kingdom. We propose the 'conditional estimation parity' metric, which accounts for estimation error and the underlying data-generating process. Our approach bridges the divide between machine learning fairness metrics and anti-discrimination law, offering a legally grounded framework for developing non-discriminatory automated decision systems.
  arXiv  Detail & Related papers  (2024-06-29T10:59:21Z)
• Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness [1.5029560229270191]
  The topic of fairness in AI has sparked meaningful discussions in the past years. From a legal perspective, many open questions remain. The AI Act might present a tremendous step towards bridging these two approaches.
  arXiv  Detail & Related papers  (2024-03-29T09:54:09Z)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparity [3.5607241839298878]
  Empirical evidence suggests that algorithmic decisions driven by Machine Learning (ML) techniques threaten to discriminate against legally protected groups or create new sources of unfairness. This work aims at assessing up to what point we can assure legal fairness through fairness metrics and under fairness constraints. Our experiments and analysis suggest that AI-assisted decision-making can be fair from a legal perspective depending on the case at hand and the legal justification.
  arXiv  Detail & Related papers  (2023-06-14T09:38:05Z)
• Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree [5.153559154345212]
  We show that EU non-discrimination law coincides with notions of algorithmic fairness proposed in computer science literature. We set out the normative underpinnings of fairness metrics and technical interventions and compare these to the legal reasoning of the Court of Justice of the EU. We conclude with implications for AI practitioners and regulators.
  arXiv  Detail & Related papers  (2023-05-05T12:00:39Z)
• Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyond [2.608935407927351]
  We analyze the impact of the DMA and related EU acts on AI models and their underlying data across four key areas. We show how, based on CJEU jurisprudence, a coherent interpretation of the concept of non-discrimination in both traditional non-discrimination and competition law may be found.
  arXiv  Detail & Related papers  (2022-12-09T17:29:19Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence [81.04070052740596]
  Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. This paper introduces the history, the current state, and the future directions of research in LegalAI.
  arXiv  Detail & Related papers  (2020-04-25T14:45:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.