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Journal article

Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data

Abstract:

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data o...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1177/2053951717743530

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/J017728/1
Publisher:
SAGE Publications
Journal:
Big Data and Society More from this journal
Volume:
4
Issue:
2
Pages:
1-17
Publication date:
2017-11-20
Acceptance date:
2017-11-17
DOI:
EISSN:
2053-9517
Keywords:
Pubs id:
pubs:827824
UUID:
uuid:91cb5f96-4a35-4807-8736-16a035aa6784
Local pid:
pubs:827824
Source identifiers:
827824
Deposit date:
2018-03-05

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