research-article

Mitigating bias in algorithmic hiring: evaluating claims and practices

Authors:

Manish Raghavan,

Karen LevyAuthors Info & Claims

FAT* '20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Pages 469 - 481

https://doi.org/10.1145/3351095.3372828

Published: 27 January 2020 Publication History

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.

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Mitigating bias in algorithmic hiring: evaluating claims and practices

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FAT* '20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

January 2020

895 pages

ISBN:9781450369367

DOI:10.1145/3351095

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