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Fair Prediction with Endogenous Behavior

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Listed:
  • Christopher Jung
  • Sampath Kannan
  • Changhwa Lee
  • Mallesh M. Pai
  • Aaron Roth
  • Rakesh Vohra

Abstract

There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fairly." However, there are several proposed notions of fairness, typically mutually incompatible. Using criminal justice as an example, we study a model in which society chooses an incarceration rule. Agents of different demographic groups differ in their outside options (e.g. opportunity for legal employment) and decide whether to commit crimes. We show that equalizing type I and type II errors across groups is consistent with the goal of minimizing the overall crime rate; other popular notions of fairness are not.

Suggested Citation

  • Christopher Jung & Sampath Kannan & Changhwa Lee & Mallesh M. Pai & Aaron Roth & Rakesh Vohra, 2020. "Fair Prediction with Endogenous Behavior," Papers 2002.07147, arXiv.org.
    • Handle: RePEc:arx:papers:2002.07147
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    References listed on IDEAS

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    Cited by:

    1. Annie Liang & Jay Lu & Xiaosheng Mu & Kyohei Okumura, 2021. "Algorithm Design: A Fairness-Accuracy Frontier," Papers 2112.09975, arXiv.org, revised May 2024.
    2. Charlson, G., 2022. "Digital Gold? Pricing, Inequality and Participation in Data Markets," Janeway Institute Working Papers 2225, Faculty of Economics, University of Cambridge.
    3. Charlson, G., 2022. "Digital gold? Pricing, inequality and participation in data markets," Cambridge Working Papers in Economics 2258, Faculty of Economics, University of Cambridge.
    4. Sampath Kannan & Mingzi Niu & Aaron Roth & Rakesh Vohra, 2021. "Best vs. All: Equity and Accuracy of Standardized Test Score Reporting," Papers 2102.07809, arXiv.org.

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