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As responsible AI gains importance in machine learning algorithms, propertiessuch as fairness, adversarial robustness, and causality have receivedconsiderable attention in recent years. However, despite their individualsignificance, there remains a critical gap in simultaneously exploring andintegrating these properties. In this paper, we propose a novel approach thatexamines the relationship between individual fairness, adversarial robustness,and structural causal models in heterogeneous data spaces, particularly whendealing with discrete sensitive attributes. We use causal structural models andsensitive attributes to create a fair metric and apply it to measure semanticsimilarity among individuals. By introducing a novel causal adversarialperturbation and applying adversarial training, we create a new regularizerthat combines individual fairness, causality, and robustness in the classifier.Our method is evaluated on both real-world and synthetic datasets,demonstrating its effectiveness in achieving an accurate classifier thatsimultaneously exhibits fairness, adversarial robustness, and causal awareness.

Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces