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Affirmative Action vs. Affirmative Information

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  • Claire Lazar Reich

Abstract

Critical decisions in hiring, college admissions, and credit lending are guided by predictions made in the presence of uncertainty. While uncertainty imparts errors across all demographic groups, this paper shows that the types of errors vary systematically: Groups with higher average outcomes are typically assigned higher false positive rates, while those with lower average outcomes are assigned higher false negative rates. We characterize the conditions that give rise to this disparate impact and explain why the intuitive remedy to omit demographic variables from datasets does not correct it. Instead of data omission, this paper examines how data acquisition can broaden access to opportunity. The strategy, which we call "Affirmative Information," could stand as an alternative to Affirmative Action.

Suggested Citation

  • Claire Lazar Reich, 2021. "Affirmative Action vs. Affirmative Information," Papers 2102.10019, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2102.10019
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    References listed on IDEAS

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    1. Ashesh Rambachan & Jon Kleinberg & Sendhil Mullainathan & Jens Ludwig, 2020. "An Economic Approach to Regulating Algorithms," NBER Working Papers 27111, National Bureau of Economic Research, Inc.
    2. Vojtěch Bartoš & Michal Bauer & Julie Chytilová & Filip Matějka, 2016. "Attention Discrimination: Theory and Field Experiments with Monitoring Information Acquisition," American Economic Review, American Economic Association, vol. 106(6), pages 1437-1475, June.
    3. Jon Kleinberg & Sendhil Mullainathan, 2019. "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers 25854, National Bureau of Economic Research, Inc.
    4. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2018. "Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 22-27, May.
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