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metadata language: English

Replication data for: Algorithmic Fairness

Version
1
Resource Type
Dataset
Creator
  • Kleinberg, Jon
  • Ludwig, Jens
  • Mullainathan, Sendhil
  • Rambachan, Ashesh
Publication Date
2018-05-01
Description
  • Abstract

    Concerns that algorithms may discriminate against certain groups have led to numerous efforts to 'blind' the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preferences can change how the estimated prediction function is used (e.g., different threshold for different groups) but the function itself should not change. We show in an empirical example for college admissions that the inclusion of variables such as race can increase both equity and efficiency.
Availability
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Relations
  • Is supplement to
    DOI: 10.1257/pandp.20181018 (Text)
Publications
  • Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan. “Algorithmic Fairness.” AEA Papers and Proceedings 108 (2018): 22–27. https://doi.org/10.1257/pandp.20181018.
    • ID: 10.1257/pandp.20181018 (DOI)

Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-13

Kleinberg, Jon; Ludwig, Jens; Mullainathan, Sendhil; Rambachan, Ashesh (2018): Replication data for: Algorithmic Fairness. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E114435V1