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Statistical Discrimination in Ratings-Guided Markets

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  • Yeon-Koo Che
  • Kyungmin Kim
  • Weijie Zhong

Abstract

We study statistical discrimination of individuals based on payoff-irrelevant social identities in markets that utilize ratings and recommendations for social learning. Even though rating/recommendation algorithms can be designed to be fair and unbiased, ratings-based social learning can still lead to discriminatory outcomes. Our model demonstrates how users' attention choices can result in asymmetric data sampling across social groups, leading to discriminatory inferences and potential discrimination based on group identities.

Suggested Citation

  • Yeon-Koo Che & Kyungmin Kim & Weijie Zhong, 2020. "Statistical Discrimination in Ratings-Guided Markets," Papers 2004.11531, arXiv.org, revised Nov 2024.
    • Handle: RePEc:arx:papers:2004.11531
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    References listed on IDEAS

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    Cited by:

    1. Lepage, Louis Pierre, 2021. "Endogenous learning, persistent employer biases, and discrimination," CLEF Working Paper Series 34, Canadian Labour Economics Forum (CLEF), University of Waterloo.
    2. Federico Echenique & Anqi Li, 2022. "Rationally Inattentive Statistical Discrimination: Arrow Meets Phelps," Papers 2212.08219, arXiv.org, revised Sep 2024.

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