Fair Decision Trees
Fair Decision Trees is a decision tree learning method designed to minimize bias with respect to sensitive attributes, thereby ensuring equitable treatment across different demographic groups while maintaining predictive accuracy.
9
Publications
378
Citations
22
Authors
9
Institutions
Publications per year
2017–2026
8
Parents
9
Edward Raff, Jared Sylvester, Steven Mills · 2018 · 57 citations
Computing, Data Science, Data Mining +14
1–5 of 9
22
3
h-index
3
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2
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2
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2
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1–5 of 22
9
University of Southern California
Los Angeles, United States
3 h-index
7 publications
432 citations
California Southern University
Chandler, United States
2 h-index
6 publications
411 citations
University of Maryland, Baltimore County
Baltimore, United States
2 h-index
2 publications
101 citations
Booz Allen Hamilton (United States)
Tysons Corner, United States
1 h-index
3 publications
171 citations

University of Milano-Bicocca
Milan, Italy
1 h-index
4 publications
72 citations

Ghent University
Ghent, Belgium
1 h-index
3 publications
66 citations

Harvard University Press
Cambridge, United States
1 h-index
3 publications
66 citations
Leibniz University of Applied Sciences
Hanover, Germany
1 h-index
1 publications
18 citations
1–8 of 9
Conference · 2 publications · 147 citations
2
h-index
Repository · 2 publications · 30 citations
2
h-index
1 publications · 18 citations
1
h-index
1 publications · 22 citations
1
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1–4