Concerns regarding unfairness and discrimination in the context of artificial
intelligence (AI) systems have recently received increased attention from both
legal and computer science scholars. Yet, the degree of overlap between notions
of algorithmic bias and fairness on the one hand, and legal notions of
discrimination and equality on the other, is often unclear, leading to
misunderstandings between computer science and law. What types of bias and
unfairness does the law address when it prohibits discrimination? What role can
fairness metrics play in establishing legal compliance? In this paper, we aim
to illustrate to what extent European Union (EU) non-discrimination law
coincides with notions of algorithmic fairness proposed in computer science
literature and where they differ. The contributions of this paper are as
follows. First, we analyse seminal examples of algorithmic unfairness through
the lens of EU non-discrimination law, drawing parallels with EU case law.
Second, 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. Specifically, we show how normative assumptions often
remain implicit in both disciplinary approaches and explain the ensuing
limitations of current AI practice and non-discrimination law. We conclude with
implications for AI practitioners and regulators.

本研究旨在分析欧洲联盟的非歧视法与计算机科学领域先进的算法公正概念之间的重叠与区别，研究表明，当前的 AI 实践和非歧视法存在的局限性，对 AI 从业者和监管者有着重要的启示。

欧盟非歧视法视角下的算法不公平：为何法律不是决策树

Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or  Why the Law is not a Decision Tree

This article identifies a critical incompatibility between European notions
of discrimination and existing statistical measures of fairness. First, we
review the evidential requirements to bring a claim under EU non-discrimination
law. Due to the disparate nature of algorithmic and human discrimination, the
EU's current requirements are too contextual, reliant on intuition, and open to
judicial interpretation to be automated. Second, we show how the legal
protection offered by non-discrimination law is challenged when AI, not humans,
discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes,
prejudice) and unintentional biases (e.g. organisational practices or
internalised stereotypes) which can act as a signal to victims that
discrimination has occurred. Finally, we examine how existing work on fairness
in machine learning lines up with procedures for assessing cases under EU
non-discrimination law. We propose "conditional demographic disparity" (CDD) as
a standard baseline statistical measurement that aligns with the European Court
of Justice's "gold standard." Establishing a standard set of statistical
evidence for automated discrimination cases can help ensure consistent
procedures for assessment, but not judicial interpretation, of cases involving
AI and automated systems. Through this proposal for procedural regularity in
the identification and assessment of automated discrimination, we clarify how
to build considerations of fairness into automated systems as far as possible
while still respecting and enabling the contextual approach to judicial
interpretation practiced under EU non-discrimination law.
N.B. Abridged abstract

通过对非歧视法的案例进行研究，提出了一种指标条件人口差异（CDD）来衡量机器学习中的公平性，此举可促进机器学习中公平性考虑，并尊重欧盟非歧视法中的情境因素。