Mitigating Nonlinear Algorithmic Bias in Binary Classification
This work addresses fairness issues in AI for stakeholders like credit applicants, but it is incremental as it applies an existing causal method to a specific nonlinear bias case.
The paper tackled nonlinear algorithmic bias in binary classification by using causal modeling with a higher-order polynomial term on the German Credit dataset, showing improved fairness with little impact on overall accuracy.
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 age bias and the problem of binary classification. We show that the probability of getting correctly classified as "low risk" is lowest among young people. The probability increases with age nonlinearly. To incorporate the nonlinearity into the causal model, we introduce a higher order polynomial term. Based on the fitted causal model, the de-biased probability estimates are computed, showing improved fairness with little impact on overall classification accuracy. Causal modeling is intuitive and, hence, its use can enhance explicability and promotes trust among different stakeholders of AI.