%0 Journal Article 
%A Carrizosa, Emilio
%A Jiménez-Llamas, Rafael
%A Ramírez Cobo, Josefa
%T Fair Logistic Regression via Penalized Mean-Field Variational Inference
%D 2025
%U http://hdl.handle.net/10498/38832
%X Fairness in Machine Learning (ML) has recently risen in importance due to its
demand by both society and institutions, either private or public. This is due to
the discrimination or disparities that may arise as a consequence of biases in the
data sets that are used for training the prediction algorithms. As a consequence,
the applicability of such algorithms may be limited. In this paper we propose a fair
version of the logistic regression via Mean-Field Variational Bayes (MFVB). Fairness
is defined in terms of a carefully selected metric that, not only is interpretable for the
considered logistic regression problem, but also leads to tractable MFVB equations.
In addition, it allows for the problem to be solved in a private manner, further
increasing the applicability of the method. The MFVB equations are solved through
a Coordinate Ascent Variational Inference (CAVI) algorithm, where a proper selection
of the hyper-parameters is shown to be crucial to assure accurate classification rates.
Numerical experiments illustrate how the novel approach provides a trade-off between
the accuracy of the method and the predictions’ level of fairness that eases the decision making process.
%K Fairness
%K Variational Bayes
%K Binary Classification
%K Coordinate Ascent Variational Inference
%~ Universidad de Cádiz