Title: Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices

Abstract: There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their practices, focusing particularly on efforts to detect and mitigate bias. Our analysis considers both technical and legal perspectives. Technically, we consider the various choices vendors make regarding data collection and prediction targets, and explore the risks and trade-offs that these choices pose. We also discuss how algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law.

Bio (Manish Raghavan): Manish is a final-year PhD candidate at Cornell University advised by Jon Kleinberg. He works at the intersection of machine learning, discrimination law, and algorithmic economics. His research focuses primarily on aspects of fairness, equity, and discrimination in socially consequential uses of algorithmic decision-making, particularly in hiring. He also works on issues of transparency in algorithmic decision-making from both theoretical and policy-oriented perspectives.