Fairness Constraints in High-Dimensional Generalized Linear Models

תקציר מקורי באנגליתarXiv:2604.16610v3 Announce Type: replace-cross Abstract: Most fairness-aware learning methods assume that sensitive attributes are observed, an assumption that may fail due to privacy, legal, or data-collection constraints. We develop a framework for fairness-aware generalized linear models when the sensitive attribute is latent, possibly multi-category, and the predictors may be high-dimensional. Candidate proxy variables are modeled using Gaussian mixtures for continuous predictors and product-multinomial mixtures for categorical predictors. The resulting posterior group-membership probabilities are used to residualize the predictors and reduce their association with the latent sensitive structure. For continuous outcomes, we use constrained least squares to limit the contribution of th