Counterfactual Fairness through Transforming Data Orthogonal to Bias

arXiv:2403.17852v1 Announce Type: new
Abstract: Machine learning models have shown exceptional prowess in solving complex issues across various domains. Nonetheless, these models can sometimes exhibit biased decision-making, leading to disparities in treatment across different groups. Despite the extensive research on fairness, the nuanced effects of multivariate and continuous sensitive variables on decision-making outcomes remain insufficiently studied. We introduce a novel data pre-processing algorithm, Orthogonal to Bias (OB), designed to remove the influence of a group of continuous sensitive variables, thereby …

abstract arxiv bias continuous counterfactual cs.lg data decision domains effects fairness machine machine learning machine learning models making multivariate research stat.ml through treatment type variables