mlsanityData to 7 Oct 2026

Interventions Against Machine-Assisted Statistical Discrimination

arXiv 2310.04585 ↗First posted 6 Oct 2023econ.TH cs.LGcited by 0

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Abstract (arXiv)

I study statistical discrimination driven by verifiable beliefs, such as those generated by machine learning, rather than by humans. When beliefs are verifiable, interventions against statistical discrimination can move beyond simple belief-free designs, like affirmative action and blinding, to more sophisticated belief-contingent ones. I analyze a belief-contingent intervention, common identity, and show that it can be more effective at combating statistical discrimination than popular alternatives -- particularly when the training dataset exhibits the kinds of statistical biases that often plague machine-assisted decision problems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 101 references that are in the map. First posted on arXiv 6 Oct 2023.

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References · 26

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
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14 more
  1. 2019Discrimination in the Age of Algorithmscited by 51
  2. 2022Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairnesscited by 45
  3. 2017A Short-term Intervention for Long-term Fairness in the Labor Marketcited by 44
  4. 2019The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationallycited by 41
  5. 2019Fair Classification and Social Welfarecited by 40
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  7. 2022Algorithmic Assistance with Recommendation-Dependent Preferencescited by 11
  8. 2020Fair Prediction with Endogenous Behaviorcited by 10
  9. 2021On the Fairness of Machine-Assisted Human Decisionscited by 10
  10. 2019Dynamic Modeling and Equilibria in Fair Decision Makingcited by 8
  11. 2018Eliminating Latent Discrimination: Train Then Maskcited by 4
  12. 2021Unpacking the Black Box: Regulating Algorithmic Decisionscited by 3
  13. 2020Statistical Discrimination in Ratings-Guided Marketscited by 1
  14. 2023Algorithms, Incentives, and Democracycited by 1

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