Open xixiaoyin opened 1 year ago
A Convex Framework for Fair Regression (Richard et al., 2017)
Individual Fairness
Group Fairness
Hybrid notions of fairness
Minimizing, in which f is the fairness penalty, and use l2 regularization to prevent overfitting.
Comparing pairs of instances (one from each group) as cross pairs, essential Individual fairness, all under the notion that similar instances should be treated similarly
Difference is that whether unfairness can be cancelled or not
D(yi,yj) is inversely proportional to | yi − yj |
Does not rule out perfect predictor (Like AOD and EOD)
Whether to use sensitive attributes in learning process does not affect much
Transfer classification to regression by Pr[yi = 1] = exp(w · xi)/(1 + exp(w · xi))
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
Statistical parity: f(X) is independent of the protected attribute A.
Bounded group loss (BGL):