Debiasing Machine Learning Models by Using Weakly Supervised Learning

arXiv:2402.15477v1 Announce Type: new
Abstract: We tackle the problem of bias mitigation of algorithmic decisions in a setting where both the output of the algorithm and the sensitive variable are continuous. Most of prior work deals with discrete sensitive variables, meaning that the biases are measured for subgroups of persons defined by a label, leaving out important algorithmic bias cases, where the sensitive variable is continuous. Typical examples are unfair decisions made with respect to the age or the financial …

abstract algorithm arxiv bias biases continuous cs.cy cs.lg deals decisions machine machine learning machine learning models meaning prior subgroups supervised learning the algorithm type variables work