Fairness Generalization
Fairness Generalization is a theoretical framework in machine learning that characterizes the ability of a model to maintain fair decision-making performance across diverse, previously unseen domains or distributions by generalizing fairness-aware patterns beyond the training environment.
15
Publications
596
Citations
42
Authors
23
Institutions
Publications per year
2017–2026
12
Parents
15
Preserving Fairness Generalization in Deepfake Detection
Yuri Nakao, Lorenzo Strappelli, Simone Stumpf et al. · International Journal of Human-Computer Interaction · 2022 · 45 citations
Eliciting and Enforcing Subjective Individual Fairness.
Christopher Jung, Michael Kearns, Seth Neel et al. · arXiv (Cornell University) · 2019 · 42 citations · Full text
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Emory University
Atlanta, United States
2 h-index
2 publications
148 citations
The University of Texas at Austin
Austin, United States
2 h-index
2 publications
148 citations
Universidad del Noreste
Tampico, Mexico
2 h-index
4 publications
100 citations
Nanchang University
Nanchang, China
1 h-index
2 publications
150 citations

Philadelphia University
Philadelphia, United States
1 h-index
3 publications
90 citations
The University of Texas at Dallas
Richardson, United States
1 h-index
4 publications
96 citations
Association for Symbolic Logic
Storrs, United States
1 h-index
1 publications
42 citations
The Ohio State University
Columbus, United States
1 h-index
1 publications
11 citations
1–8 of 23
Repository · 4 publications · 98 citations
4
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1 publications · 126 citations
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Repository · 1 publications · 67 citations
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Repository · 1 publications · 22 citations
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1 publications · 16 citations
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