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Building Nondiscriminatory Algorithms in Selected Data.

David Arnold1, Will Dobbie2, Peter Hull3

  • 1University of California, San Diego and NBER.

American Economic Review. Insights
|June 23, 2025
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Summary
This summary is machine-generated.

We created new tools to combat algorithmic discrimination by identifying and correcting biased data inputs. Our methods ensure fairer algorithms and improve prediction accuracy, even with incomplete outcome data.

Keywords:
C26J15K42

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Area of Science:

  • Computer Science
  • Economics
  • Law

Background:

  • Algorithmic discrimination occurs when input data systematically differs for individuals with similar potential outcomes.
  • Selective observability of outcomes complicates the detection and mitigation of algorithmic bias.

Purpose of the Study:

  • To develop quasi-experimental tools for understanding and eliminating algorithmic discrimination.
  • To build non-discriminatory algorithms for selectively observed outcomes.
  • To improve prediction accuracy by addressing data disparities.

Main Methods:

  • Developed new quasi-experimental methodologies to analyze algorithmic fairness.
  • Quantified conditional input disparities linked to algorithmic discrimination.
  • Implemented methods to measure and purge these disparities.
  • Utilized quasi-random bail judge assignments in New York City for empirical validation.

Main Results:

  • Confirmed that algorithmic discrimination stems from systematic input differences.
  • Demonstrated that measuring and purging input disparities eliminates algorithmic discrimination.
  • Showcased improved prediction accuracy through corrected selective observability.
  • Validated the effectiveness of the developed algorithms in a real-world setting.

Conclusions:

  • The developed quasi-experimental tools effectively identify and mitigate algorithmic discrimination.
  • Addressing conditional input disparities is crucial for building fair and accurate algorithms.
  • The approach enhances predictive performance by accounting for selective outcome observation.