科研成果详情

This paper addresses the critical but often overlooked problem of exploring latent sensitive attributes in machine-learning datasets where they are not explicitly present. We propose a method named Object-Relational Causal Inference (ORCI), solving the problem with good reusability. We mathematically demonstrate the enhancement of data fairness by uncovering latent sensitive attributes and build a framework to tackle the latent discrimination exploration challenge. Firstly, we highlight the limitations of classical causal discovery methods and introduce an approximate exhaustive search algorithm called BIC-based Exhaustive Search (BICES). Then, we integrate the Object Relational (OR) approach into causal modeling, enabling the subsequent structured probabilistic inference based on the OR causal model to derive fair and effective latent sensitive attributes. We conduct simulations and experiments on synthetic and real-world cases across various machine-learning tasks, with discrete and continuous data, and in diverse settings. Results show that our method successfully uncovers latent sensitive attributes, enhancing the data fairness while preserving data utility. Besides, our detailed theoretical derivations and rich experiments demonstrate the generalization of our method.