Calibration Fairness
Calibration Fairness is a fairness metric in machine learning that requires a predictive model to produce calibrated probabilities—where the predicted probability of an outcome matches the true likelihood of that outcome—across all defined demographic groups, thereby ensuring group-level equivalence in the reliability of predicted scores.
8
Publications
490
Citations
27
Authors
13
Institutions
Publications per year
2017–2026
8
8
Geoff Pleiss, Manish Raghavan, Felix Wu et al. · arXiv (Cornell University) · 2017 · 290 citations · Full text
Calibration for the (Computationally-Identifiable) Masses
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold et al. · arXiv (Cornell University) · 2017 · 49 citations · Full text
From Soft Classifiers to Hard Decisions
Ran Canetti, Aloni Cohen, Nishanth Dikkala et al. · 2019 · 41 citations · Full text
Fairness through Equality of Effort
Wen Huan, Yongkai Wu, Lu Zhang et al. · Companion Proceedings of the Web Conference 2020 · 2020 · 22 citations · Full text
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Stanford University
Stanford, United States
2 h-index
5 publications
61 citations
Moscow Institute of Thermal Technology
Moscow, Russia
1 h-index
3 publications
123 citations
Stanford Medicine
Stanford, United States
1 h-index
1 publications
14 citations
Boston University
Boston, United States
1 h-index
4 publications
164 citations

LMU Klinikum
Munich, Germany
1 h-index
1 publications
21 citations
Ludwig-Maximilians-Universität München
Munich, Germany
1 h-index
1 publications
21 citations
New York University
New York, United States
1 h-index
1 publications
42 citations

IT University of Copenhagen
Copenhagen, Denmark
1 h-index
1 publications
21 citations
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Repository · 4 publications · 364 citations
4
h-index
1 publications · 42 citations
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Conference · 1 publications · 22 citations
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1 publications · 11 citations
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