Detecting and Mitigating Algorithmic Bias in Binary Classification using Causal Modeling
It addresses algorithmic bias for binary classification tasks, offering an incremental method to enhance explainability and trust.
The paper tackled algorithmic gender bias in binary classification by using causal modeling to detect and mitigate bias, showing statistically significant bias at the 0.05 level and a slight improvement in overall classification accuracy.
This paper proposes the use of causal modeling to detect and mitigate algorithmic bias. We provide a brief description of causal modeling and a general overview of our approach. We then use the Adult dataset, 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 gender bias and the problem of binary classification. We show that gender bias in the prediction model is statistically significant at the 0.05 level. We demonstrate the effectiveness of the causal model in mitigating gender bias by cross-validation. Furthermore, we show that the overall classification accuracy is improved slightly. Our novel approach is intuitive, easy-to-use, and can be implemented using existing statistical software tools such as "lavaan" in R. Hence, it enhances explainability and promotes trust.