A minimax framework for quantifying risk-fairness trade-off in regression
量化回归风险与公平性权衡的极小极大框架
We propose a theoretical framework for the problem of learning a real-valued function which meets fairness requirements. This framework is built upon the notion of α-relative (fairness) improvement of the regression function which we introduce using the theory of optimal transport. Setting α=0 corresponds to the regression problem under the Demographic Parity constraint, while α=1 corresponds to the classical regression problem without any constraints. For α∈(0,1) the…