Fairness-Aware Neural R\'eyni Minimization for Continuous Features

Understand

The past few years have seen a dramatic rise of academic and societal interest in fair machine learning.

• While plenty of fair algorithms have been proposed recently to tackle this challenge for discrete variables, only a few ideas exist for continuous ones.
• The objective in this paper is to ensure some independence level between the outputs of regression models and any given continuous sensitive variables.
• For this purpose, we use the Hirschfeld-Gebelein-R\'enyi (HGR) maximal correlation coefficient as a fairness metric.