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Algorithms as discrimination detectors.

Jon Kleinberg1, Jens Ludwig2, Sendhil Mullainathan3

  • 1Department of Computer Science, Cornell University, Ithaca, NY 14853; kleinberg@cornell.edu.

Proceedings of the National Academy of Sciences of the United States of America
|July 30, 2020
PubMed
Summary
This summary is machine-generated.

Algorithms can help detect and prevent discrimination by offering greater decision-making specificity than humans. Regulatory changes can leverage this algorithmic precision to combat unfair bias.

Keywords:
algorithmsdiscriminationmachine learning

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Area of Science:

  • Computer Science
  • Law
  • Sociology

Background:

  • Detecting discrimination is challenging, especially with human decision-making.
  • Current algorithms may inadvertently increase discrimination risks.

Purpose of the Study:

  • To explore how algorithms can be used to detect and prevent discrimination.
  • To examine the potential of algorithmic specificity in combating bias.

Main Methods:

  • Analysis of algorithmic decision-making processes.
  • Comparison of algorithmic versus human decision-making specificity.
  • Review of legal and regulatory frameworks.

Main Results:

  • Algorithms necessitate higher specificity than human decisions.
  • This specificity allows for more detailed probing of decision-making aspects.
  • Algorithms can potentially enhance discrimination detection.

Conclusions:

  • Algorithms, with appropriate legal and regulatory adjustments, can be powerful tools for detecting and preventing discrimination.
  • Leveraging algorithmic specificity is key to mitigating bias.