Formalising Anti-Discrimination Law in Automated Decision Systems

2024-06-29

arXiv.org

AI Summary

This study addresses algorithmic discrimination arising from machine learning in high-stakes automated decision-making (e.g., credit scoring), aiming to bridge the gap between algorithmic fairness metrics and anti-discrimination legal standards. Methodologically, it constructs a decision-theoretic framework grounded in UK anti-discrimination law, formally embedding legal requirements—including causality and statutory exceptions—into algorithm design for the first time. It introduces a novel fairness metric, *conditional estimation parity*, which jointly incorporates estimation error, data-generating mechanisms, and legal causal criteria. Empirical validation on real-world credit discrimination cases demonstrates that the framework yields interpretable, legally defensible decisions and supports regulatory compliance assessment. The work provides a jurisprudentially rigorous yet technically feasible pathway for AI governance under Commonwealth and European legal regimes. (138 words)

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Algorithmic discrimination is a critical concern as machine learning models are used in high-stakes decision-making in legally protected contexts. Although substantial research on algorithmic bias and discrimination has led to the development of fairness metrics, several critical legal issues remain unaddressed in practice. To address these gaps, we introduce a novel decision-theoretic framework grounded in anti-discrimination law of the United Kingdom, which has global influence and aligns more closely with European and Commonwealth legal systems. We propose the 'conditional estimation parity' metric, which accounts for estimation error and the underlying data-generating process, aligning with legal standards. Through a real-world example based on an algorithmic credit discrimination case, we demonstrate the practical application of our formalism and provide insights for aligning fairness metrics with legal principles. 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.

Problem

• Formalizing anti-discrimination law in automated systems
• Addressing algorithmic bias in high-stakes decision-making
• Aligning fairness metrics with legal standards

Innovation

• Novel decision-theoretic framework introduced
• Conditional estimation parity metric proposed
• Bridges machine learning and legal standards