Mitigating Nonlinear Algorithmic Bias in Binary Classification

arXiv:2312.05429v3 Announce Type: replace
Abstract: This paper proposes the use of causal modeling to detect and mitigate algorithmic bias that is nonlinear in the protected attribute. We provide a general overview of our approach. We use the German Credit data set, which is available for download from the UC Irvine Machine Learning Repository, to develop (1) a prediction model, which is treated as a black box, and (2) a causal model for bias mitigation. In this paper, we focus on …

abstract algorithmic bias arxiv bias binary causal classification credit cs.cy cs.lg data data set download general german machine machine learning modeling overview paper set stat.ap type uc irvine