Directly Discriminatory Algorithms

Discriminatory bias in algorithmic systems is widely documented. How should the law respond? A broad consensus suggests approaching the issue principally through the lens of indirect discrimination, focusing on algorithmic systems' impact. In this article, we set out to challenge this analysis, arguing that while indirect discrimination law has an important role to play, a narrow em on this regime in the context of machine learning algorithms is both normatively undesirable and legally flawed. We illustrate how certain forms of algorithmic bias in frequently deployed algorithms might constitute direct discrimination, and explore the ramifications-both in practical terms, and the broader challenges automated decision-making systems pose to the conceptual apparatus of anti-discrimination law.

Automated summary

Discriminatory bias in algorithmic systems is a well-documented issue. The law should respond to this problem by approaching it through the lens of indirect discrimination and focusing on the impact of algorithmic systems. However, this article argues that the narrow focus on indirect discrimination is both normatively undesirable and legally flawed in the context of machine learning algorithms. The article explores how certain forms of algorithmic bias in commonly used algorithms might constitute direct discrimination and the broader implications this poses to anti-discrimination law.