R. Jiménez Llamas, E. Carrizosa Priego, P. Ramirez Cobo

Fairness in machine learning (ML) is a recent and prominent research area with the aim of correcting discriminatory solutions in prediction algorithms caused by biases in the training dataset. In this work we present a novel method for fairness in logistic regression based on variational inference and the Mean Field approximation. Specifically, we consider a penalization term, proportional to the unfairness degree of the solution, that is chosen in a way that allows for a simple modification of the well-known CAVI iterative method. As a result, the computational cost of the algorithm does not increase significantly for high dimensions. Additionally, the method provides a clear tradeoff between the accuracy of the prediction model and the fairness degree of the solution, a fact that facilitates decision-making. The novel method shall be illustrated using both simulated and real datasets.

Keywords: Empirical Bayes, Fairness, Machine Learning, Variational Inference, Logistic Regression.


GT11.BAYES2 Invited Session
November 7, 2023  6:40 PM
CC1: Audience

Other papers in the same session

Cookie policy

We use cookies in order to be able to identify and authenticate you on the website. They are necessary for the correct functioning of it, and therefore they can not be disabled. If you continue browsing the website, you are agreeing with their acceptance, as well as our Privacy Policy.

Additionally, we use Google Analytics in order to analyze the website traffic. They also use cookies and you can accept or refuse them with the buttons below.

You can read more details about our Cookie Policy and our Privacy Policy.