3 research outputs found
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Adversarial perturbation is used to expose vulnerabilities in machine
learning models, while the concept of individual fairness aims to ensure
equitable treatment regardless of sensitive attributes. Despite their initial
differences, both concepts rely on metrics to generate similar input data
instances. These metrics should be designed to align with the data's
characteristics, especially when it is derived from causal structure and should
reflect counterfactuals proximity. Previous attempts to define such metrics
often lack general assumptions about data or structural causal models. In this
research, we introduce a causal fair metric formulated based on causal
structures that encompass sensitive attributes. For robustness analysis, the
concept of protected causal perturbation is presented. Additionally, we delve
into metric learning, proposing a method for metric estimation and deployment
in real-world problems. The introduced metric has applications in the fields
adversarial training, fair learning, algorithmic recourse, and causal
reinforcement learning
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
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