A received wisdom is that automated decision-making serves as an anti-bias intervention. The conceit is that removing humans from the decision-making process will also eliminate human bias. The paradox, however, is that in some instances, automated decision-making has served to replicate and amplify bias. In this presentation, Dr. Ajunwa will use the case study of algorithmic capture of hiring as a heuristic device to provide a taxonomy of problematic features associated with algorithmic decision-making as an anti-bias intervention, arguing that those features are at odds with the fundamental principle of equal opportunity employment. To examine these features and explore potential legal approaches for rectifying them, Dr. Ajunwa brings together two streams of legal scholarship: law and technology studies and employment & labor law.