Preprocessing Bias Mitigation
Preprocessing Bias Mitigation is a method in algorithmic fairness that modifies training data to reduce biases with respect to sensitive attributes before model learning occurs, distinguishing itself from in-processing and post-processing approaches by addressing bias at the data transformation stage.
20
Publications
3K
Citations
61
Authors
23
Institutions
Publications per year
2017–2026
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Stanford University
Stanford, United States
2 h-index
8 publications
190 citations
Yale University
New Haven, United States
2 h-index
6 publications
123 citations

Wake Forest University
Winston-Salem, United States
2 h-index
3 publications
108 citations

Universitat Jaume I
Castellon, Spain
1 h-index
3 publications
1.1K citations

Universidade do Vale do Rio dos Sinos
São Leopoldo, Brazil
1 h-index
5 publications
940 citations

Universidad de Granada
Granada, Spain
1 h-index
3 publications
936 citations
Universität Hamburg
Hamburg, Germany
1 h-index
2 publications
430 citations

Universidad de Jaén
Jaén, Spain
1 h-index
1 publications
312 citations
1–8 of 23
Repository · 3 publications · 63 citations
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2 publications · 1.4K citations
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Repository · 2 publications · 28 citations
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1 publications · 352 citations
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1 publications · 312 citations
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