Bias Preservation in Machine Learning: The Legality of Fairness Metrics Under EU Non-Discrimination Law

Abstract

Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes and increase the inequality experienced by historically disadvantaged groups. Recognising this problem, much work has emerged in recent years to test for bias in machine learning and AI systems using various fairness and bias metrics. Often these metrics address technical bias but ignore the underlying causes of inequality. In this paper we make three contributions. First, we assess the compatibility of fairness metrics used in machine learning against the aims and purpose of EU non-discrimination law. We show that the fundamental aim of the law is not only to prevent ongoing discrimination, but also to change society, policies, and practices to ‘level the playing field’ and achieve substantive rather than merely formal equality. Based on this, we then propose a novel classification scheme for fairness metrics in machine learning based on how they handle pre-existing bias and thus align with the aims of non-discrimination law. Specifically, we distinguish between ‘bias preserving’ and ‘bias transforming’ fairness metrics. Our classification system is intended to bridge the gap between non-discrimination law and decisions around how to measure fairness in machine learning and AI in practice. Finally, we show that the legal need for justification in cases of indirect discrimination can impose additional obligations on developers, deployers, and users that choose to use bias preserving fairness metrics when making decisions about individuals because they can give rise to prima facie discrimination. To achieve substantive equality in practice, and thus meet the aims of the law, we instead recommend using bias transforming metrics. To conclude, we provide concrete recommendations including a user-friendly checklist for choosing the most appropriate fairness metric for uses of machine learning and AI under EU non-discrimination law.

Keywords: artificial intelligence, machine learning, law, discrimination, non-discrimination, anti-discrimination, algorithms, European Union, bias, fairness, automated decision-making, fairness metrics, equality, substantive equality, formal equality, auditing