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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.
Summary
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.
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.
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