Blind pareto fairness and subgroup robustness
In this work we formulate and analyze subgroup robustness, particularly in the context of fairness without demographics or labels. Our goal is to recover a model that minimizes the risk of the worst-case partition of the input data subject to a minimum size constraint, while we additionally constrain this model to be Pareto efficient w.r.t. the low-risk population as well. This means that we are optimizing for the worst unknown subgroup without causing unnecessary harm on the rest of the data. We show that it is possible to protect high risk groups without explicit knowledge of their number or structure, only the size of the smallest one, and that there is a minimum partition size under which the random classifier is the only minimax option for cross-entropy and Brier score losses.
Jun 10, 2021
