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Fast Fair Regression via Efficient Approximations of Mutual Information
Most work in algorithmic fairness to date has focused on discrete outcomes, such as deciding whether to grant someone a loan or not.
Fairness Measures for Regression via Probabilistic Classification
For the regression setting we introduce tractable approximations of the independence, separation and sufficiency criteria by observing that they factorise as ratios of different conditional probabilities of the protected attributes.
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