Journal article
The unfairness of fair machine learning: leveling down and strict egalitarianism by default
- Abstract:
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In recent years, fairness in machine learning (ML), artificial intelligence (AI), and algorithmic decision-making systems has emerged as a highly active area of research and development. To date, most measures and methods to mitigate bias and improve fairness in algorithmic systems have been built in isolation from policymaking and civil societal contexts and lack serious engagement with philosophical, political, legal, and economic theories of equality and distributive justice. Many current ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.8MB, Terms of use)
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- Publisher copy:
- 10.36645/mtlr.30.1.unfairness
Authors
- Publisher:
- University of Michigan
- Journal:
- Michigan Technology Law Review More from this journal
- Volume:
- 30
- Issue:
- 1
- Article number:
- 3
- Publication date:
- 2024-01-01
- Acceptance date:
- 2023-04-02
- DOI:
- EISSN:
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2688-5484
- ISSN:
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2688-4941
- Language:
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English
- Pubs id:
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1328594
- Local pid:
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pubs:1328594
- Deposit date:
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2023-05-19
- ARK identifier:
Terms of use
- Copyright date:
- 2024
- Notes:
- This work was originally published as Brent Mittelstadt, Sandra Wachter & Chris Russell, The Unfairness of Fair Machine Learning: Leveling Down and Strict Egalitarianism by Default, 30 MICH. TECH. L. REV. (2024).
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