RT journal article
T1 Variational inference for responsible logistic regression
A1 Carrizosa, Emilio
A1 Jiménez-Llamas, Rafael
A1 Ramírez Cobo, Josefa
A2 Estadística e Investigación Operativa
K1 Fairness
K1 Sparsity
K1 Bayesian Logistic Regression
K1 Coordinate Ascent Variational Inference. 1
AB Machine Learning is increasingly used in high-stakes applications such as health-care or finance, where interpretability, transparency and fairness are critical. Thesedemands have led to growing interest in models that can meet such requirements.Logistic regression remains a popular choice in these contexts due to its versatilityand interpretability. While sparsity has been explored extensively and fairness onlyrecently, few works have considered both simultaneously. In this work, we presenta novel approach to logistic regression based on Mean-Field Variational Inference(MFVI), which integrates both sparsity and fairness into a unified Bayesian frame-work. We do so by modifying the KL divergence term in the variational objectivewith a penalization that encourages fair solutions, while also incorporating a sparsity-inducing prior that allows for feature selection. We further adapt the inference process to include group sparsity, enabling the selection or elimination of grouped features such as dummy variables derived from categorical inputs. The numerical results show a triple trade-off between accuracy, fairness and interpretability which may significantly improve the user’s capability to properly balance the three criteria.
YR 2025
FD 2025-09
LK http://hdl.handle.net/10498/38833
UL http://hdl.handle.net/10498/38833
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026