Formalising Anti-Discrimination Law in Automated Decision Systems

• URL: http://arxiv.org/abs/2407.00400v1
• Date: Sat, 29 Jun 2024 10:59:21 GMT
• Title: Formalising Anti-Discrimination Law in Automated Decision Systems
• Authors: Holli Sargeant, Måns Magnusson,
• Abstract summary: We study the legal challenges in automated decision-making by analysing conventional algorithmic fairness approaches. By translating principles of anti-discrimination law into a decision-theoretic framework, we formalise discrimination. We propose a new, legally informed approach to developing systems for automated decision-making.
• Score: 1.560976479364936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the legal challenges in automated decision-making by analysing conventional algorithmic fairness approaches and their alignment with antidiscrimination law in the United Kingdom and other jurisdictions based on English common law. By translating principles of anti-discrimination law into a decision-theoretic framework, we formalise discrimination and propose a new, legally informed approach to developing systems for automated decision-making. Our investigation reveals that while algorithmic fairness approaches have adapted concepts from legal theory, they can conflict with legal standards, highlighting the importance of bridging the gap between automated decisions, fairness, and anti-discrimination doctrine.

Related papers

• Randomization Techniques to Mitigate the Risk of Copyright Infringement [48.75580082851766]
  We investigate potential randomization approaches that can complement current practices for copyright protection. This is motivated by the inherent ambiguity of the rules that determine substantial similarity in copyright precedents. Similar randomized approaches, such as differential privacy, have been successful in mitigating privacy risks.
  arXiv  Detail & Related papers  (2024-08-21T20:55:00Z)
• 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)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• 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)
• Causal Fairness for Outcome Control [68.12191782657437]
  We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
  arXiv  Detail & Related papers  (2023-06-08T09:31:18Z)
• 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)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• 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)
• Transparency, Compliance, And Contestability When Code Is(n't) Law [91.85674537754346]
  Both technical security mechanisms and legal processes serve as mechanisms to deal with misbehaviour according to a set of norms. While they share general similarities, there are also clear differences in how they are defined, act, and the effect they have on subjects. This paper considers the similarities and differences between both types of mechanisms as ways of dealing with misbehaviour.
  arXiv  Detail & Related papers  (2022-05-08T18:03:07Z)
• Towards a Flexible Framework for Algorithmic Fairness [0.8379286663107844]
  In recent years, many different definitions for ensuring non-discrimination in algorithmic decision systems have been put forward. We present an algorithm that harnesses optimal transport to provide a flexible framework to interpolate between different fairness definitions.
  arXiv  Detail & Related papers  (2020-10-15T16:06:53Z)
• Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI [10.281644134255576]
  Article identifies a critical incompatibility between European notions of discrimination and existing statistical measures of fairness. We show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. We propose "conditional demographic disparity" (CDD) as a standard baseline statistical measurement.
  arXiv  Detail & Related papers  (2020-05-12T16:30:12Z)

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.