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An Outcome Test of Discrimination for Ranked Lists

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  • Jonathan Roth
  • Guillaume Saint-Jacques
  • YinYin Yu

Abstract

This paper extends Becker (1957)'s outcome test of discrimination to settings where a (human or algorithmic) decision-maker produces a ranked list of candidates. Ranked lists are particularly relevant in the context of online platforms that produce search results or feeds, and also arise when human decisionmakers express ordinal preferences over a list of candidates. We show that non-discrimination implies a system of moment inequalities, which intuitively impose that one cannot permute the position of a lower-ranked candidate from one group with a higher-ranked candidate from a second group and systematically improve the objective. Moreover, we show that that these moment inequalities are the only testable implications of non-discrimination when the auditor observes only outcomes and group membership by rank. We show how to statistically test the implied inequalities, and validate our approach in an application using data from LinkedIn.

Suggested Citation

  • Jonathan Roth & Guillaume Saint-Jacques & YinYin Yu, 2021. "An Outcome Test of Discrimination for Ranked Lists," Papers 2111.07889, arXiv.org.
    • Handle: RePEc:arx:papers:2111.07889
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    References listed on IDEAS

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    1. Kevin Lang & Ariella Kahn-Lang Spitzer, 2020. "Race Discrimination: An Economic Perspective," Journal of Economic Perspectives, American Economic Association, vol. 34(2), pages 68-89, Spring.
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    3. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    4. Castillo, Marco & Petrie, Ragan, 2010. "Discrimination in the lab: Does information trump appearance?," Games and Economic Behavior, Elsevier, vol. 68(1), pages 50-59, January.
    5. Donald W. K. Andrews & Gustavo Soares, 2010. "Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection," Econometrica, Econometric Society, vol. 78(1), pages 119-157, January.
    6. Ashesh Rambachan & Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan, 2020. "An Economic Perspective on Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 91-95, May.
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    Cited by:

    1. YinYin Yu & Guillaume Saint-Jacques, 2022. "Choosing an algorithmic fairness metric for an online marketplace: Detecting and quantifying algorithmic bias on LinkedIn," Papers 2202.07300, arXiv.org, revised Aug 2022.

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