Active Sampling for Min-Max Fairness
This work addresses fairness in machine learning models for disadvantaged groups, presenting an incremental improvement with a generalizable method.
The authors tackled the problem of optimizing min-max fairness in classification or regression models by proposing simple active sampling and reweighting strategies that update the model using data from the worst-off group at each timestep, proving convergence rates for convex problems like linear or logistic regression.
We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization. The key intuition behind our approach is to use at each timestep a datapoint from the group that is worst off under the current model for updating the model. The ease of implementation and the generality of our robust formulation make it an attractive option for improving model performance on disadvantaged groups. For convex learning problems, such as linear or logistic regression, we provide a fine-grained analysis, proving the rate of convergence to a min-max fair solution.