Structural Causal Model and Explainable AI for Detecting Bias and Fairness in Automated Credit Decisions System

Abstract

Fairness and bias concerns in AI systems arise when predictive models amplify inequities present in data, and measurement processes. Traditional fairness analysis approaches such as demographic parity, equalized odds, and individual fairness primarily operate on statistical associations and often struggle when protected attributes influence features through complex causal pathways. This paper explores causal inference modelling for identification and mitigation of fairness and bias issues in AI system. The proposed framework integrates structural causal models (SCMs) with an explainable AI method, Shapley Additive Explanations (SHAP) to provide interpretations to the AI system outcomes. The framework introduces structural causal modelling with counterfactual queries for the analysis of sources of fairness and bias through the protected variables. The framework is experimented on a synthetic loan application dataset with the results showing the effects of protected attributes (race and gender) on credit approval rates. The models also achieve average AUC score of 70% on the dataset.