September 17, 2019
Ifeoma Ajunwa, Cornell University

The Paradox of Automation as Anti-Bias Intervention

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. With a case study of the algorithmic capture of hiring as heuristic device, this Article provides a taxonomy of problematic features associated with algorithmic decision-making as anti-bias intervention and argues that those features are at odds with the fundamental principle of equal opportunity in employment. To examine these problematic features within the context of algorithmic hiring and to explore potential legal approaches to rectifying them, the Article brings together two streams of legal scholarship: law & technology studies and employment & labor law.

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About Ifeoma Ajunwa

Professor Ajunwa is an Assistant Professor of Labor & Employment Law in Cornell’s Industrial and Labor Relations School. She is also an Associate Faculty Member at Cornell Law School and a Faculty Associate at the Berkman Klein Center at Harvard University. Her research focus is on law and technology in the workplace. Her research interests are at the intersection of law and technology with a particular focus on the ethical governance of workplace technologies. Her research also concerns diversity and inclusion in the labor market and the workplace. Professor Ajunwa is the winner of the American Association of Law Schools (AALS) Derrick Bell Award for 2018.