Abstract
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 focus 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.