CIRCUS: A Causal Intervention-Based Framework for Enhancing Counterfactual Fairness in Trained Classifiers

Ensuring model fairness for preventing potential biases based on any sensitive attribute is crucial for the societal acceptance of artificial intelligence in critical applications. Among various fairness concepts, counterfactual fairness has gained prominence as it is grounded in causal inference. This concept requires that an individual's prediction in the original world remains consistent with that in the counterfactual world where the sensitive feature value is modified. In this article, we a