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We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks.
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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A note on the convergence of admm for linearly constrained convex optimization problems
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Model projection: Theory and applications to fair machine learning
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A fair classifier using kernel density estimation
Jaewoong Cho, Gyeongjo Hwang, and Changho Suh · 2020
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Data preprocessing to mitigate bias: A maximum entropy based approach
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Identifying and correcting label bias in machine learning
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FACT: A diagnostic for group fairness trade-offs
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Optimized score transformation for fair classification
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Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2018
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The cost of fairness in binary classification
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Fairness with overlapping groups; a probabilistic perspective
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It’s COMPASlicated: The messy relationship between RAI datasets and algorithmic fairness benchmarks
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Datasheets for datasets
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Optimized score transformation for consistent fair classification
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FairProjection
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