Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation. Adversarial perturbation, used to identify vulnerabilities in models, and individual fairness, aiming for equitable treatment of similar individuals, despite initial differences, both depend on metrics to generate comparable input data instances. Previous attempts to define such joint metrics often lack general assumptions about data or structural causal models and were unable to reflect counterfactual proximity. To address this, our paper introduces a causal fair metric formulated based on causal structures encompassing sensitive attributes and protected causal perturbation. To enhance the practicality of our metric, we propose metric learning as a method for metric estimation and deployment in real-world problems in the absence of structural causal models. We also demonstrate the application of our novel metric in classifiers. Empirical evaluation of real-world and synthetic datasets illustrates the effectiveness of our proposed metric in achieving an accurate classifier with fairness, resilience to adversarial perturbations, and a nuanced understanding of causal relationships.

翻译：尽管负责任的AI需要全面考量，但鲁棒性、公平性与因果性等因素常被孤立研究。用于识别模型漏洞的对抗扰动与旨在公平对待相似个体的个体公平性，尽管初始目标不同，两者均依赖度量来生成可比较的输入数据实例。以往定义此类联合度量的尝试往往缺乏对数据或结构因果模型的一般性假设，且无法反映反事实邻近性。为解决这一问题，本文提出一种基于包含敏感属性与受保护因果扰动的因果结构所构建的因果公平度量。为增强该度量的实用性，我们提出将度量学习作为在缺乏结构因果模型的实际问题中估计与部署度量的方法。此外，我们展示了该新型度量在分类器中的应用。在真实世界与合成数据集上的实证评估表明，所提度量能有效实现兼具公平性、对抗扰动鲁棒性及对因果关系深度理解的精准分类器。