Fairness Constraints in High-Dimensional Generalized Linear Models

arXiv: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 the estimated sensitive attribute to prediction variability. For binary outcomes, we use penalized logistic regression to reduce dependence between predicted probabilities and estimated group membership. In high-dimensional settings, the procedure is combined with SEMMS variable selection to obtain sparse models. We establish identifiability and latent-class recovery results for Gaussian and categorical mixtures and derive expressions that quantify the loss of predictive power after residualization.Simulations and real-data applications demonstrate favorable accuracy-fairness performance.

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 the estimated sensitive attribute to prediction variability. For binary outcomes, we use penalized logistic regression to reduce dependence between predicted probabilities and estimated group membership. In high-dimensional settings, the procedure is combined with SEMMS variable selection to obtain sparse models. We establish identifiability and latent-class recovery results for Gaussian and categorical mixtures and derive expressions that quantify the loss of predictive power after this http URL and real-data applications demonstrate favorable accuracy-fairness performance.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2604.16610 [stat.ML]

(or arXiv:2604.16610v3 [stat.ML] for this version)

analiza aiqbench

Fairness Constraints in High-Dimensional Generalized Linear Models

Subiecte conexe