Risk mitigation in algorithmic accountability: The role of machine learning copies
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Abstract
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DOI: 10.1371/journal.pone.0241286
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References listed on IDEAS
- Veale, Michael & Binns, Reuben, 2017. "Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data," SocArXiv ustxg, Center for Open Science.
- Alice B. Popejoy & Stephanie M. Fullerton, 2016. "Genomics is failing on diversity," Nature, Nature, vol. 538(7624), pages 161-164, October.
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Cited by:
- Miller Gloria J., 2022. "Stakeholder-accountability model for artificial intelligence projects," Journal of Economics and Management, Sciendo, vol. 44(1), pages 446-494, January.
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