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Algorithmic Fairness with Feedback

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  • John W. Patty
  • Elizabeth Maggie Penn

Abstract

The field of algorithmic fairness has rapidly emerged over the past 15 years as algorithms have become ubiquitous in everyday lives. Algorithmic fairness traditionally considers statistical notions of fairness algorithms might satisfy in decisions based on noisy data. We first show that these are theoretically disconnected from welfare-based notions of fairness. We then discuss two individual welfare-based notions of fairness, envy freeness and prejudice freeness, and establish conditions under which they are equivalent to error rate balance and predictive parity, respectively. We discuss the implications of these findings in light of the recently discovered impossibility theorem in algorithmic fairness (Kleinberg, Mullainathan, & Raghavan (2016), Chouldechova (2017)).

Suggested Citation

  • John W. Patty & Elizabeth Maggie Penn, 2023. "Algorithmic Fairness with Feedback," Papers 2312.03155, arXiv.org.
    • Handle: RePEc:arx:papers:2312.03155
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    References listed on IDEAS

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    1. Alex Frankel & Navin Kartik, 2022. "Improving Information from Manipulable Data," Journal of the European Economic Association, European Economic Association, vol. 20(1), pages 79-115.
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    3. Fryer, Roland, 2007. "Belief Flipping in a Dynamic Model of Statistical Discrimination," Scholarly Articles 2955768, Harvard University Department of Economics.
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