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Counterfactual fairness

Abstract:

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discrimin...

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Publication status:
Published
Peer review status:
Reviewed (other)
Version:
Publisher's Version

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Russell, C More by this author
Publisher:
Massachusetts Institute of Technology Press Publisher's website
Volume:
30
Pages:
4067-4077
Publication date:
2017
Acceptance date:
2017-12-09
ISSN:
1049-5258
Pubs id:
pubs:924094
URN:
uri:7f6b6d7f-83f4-4d38-9991-ec15ea7c3957
UUID:
uuid:7f6b6d7f-83f4-4d38-9991-ec15ea7c3957
Local pid:
pubs:924094

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