Causal Adversarial Perturbations for Individual Fairness and Robustness
  in Heterogeneous Data Spaces
Ahmad-Reza Ehyaei Kiarash Mohammadi Amir-Hossein Karimi Samira Samadi Golnoosh Farnadi
As responsible AI gains importance in machine learning algorithms, properties
such as fairness, adversarial robustness, and causality have received
considerable attention in recent years. However, despite their individual
significance, there remains a critical gap in simultaneously exploring and
integrating these properties. In this paper, we propose a novel approach that
examines the relationship between individual fairness, adversarial robustness,
and structural causal models in heterogeneous data spaces, particularly when
dealing with discrete sensitive attributes. We use causal structural models and
sensitive attributes to create a fair metric and apply it to measure semantic
similarity among individuals. By introducing a novel causal adversarial
perturbation and applying adversarial training, we create a new regularizer
that 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 that
simultaneously exhibits fairness, adversarial robustness, and causal awareness.