Learning Fair Models without Sensitive Attributes: A Generative Approach
This addresses fairness in machine learning for scenarios where sensitive attributes are unavailable due to privacy or legal constraints, offering a novel approach to an important bottleneck.
The paper tackles the problem of learning fair models without access to sensitive attributes by proposing a generative framework that estimates these attributes from relevant features, achieving effectiveness in accuracy and fairness on real-world datasets.
Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues. The lack of sensitive attributes challenges many existing fair classifiers. Though we lack sensitive attributes, for many applications, there usually exists features or information of various formats that are relevant to sensitive attributes. For example, purchase history of a person can reflect his or her race, which would help for learning fair classifiers on race. However, the work on exploring relevant features for learning fair models without sensitive attributes is rather limited. Therefore, in this paper, we study a novel problem of learning fair models without sensitive attributes by exploring relevant features. We propose a probabilistic generative framework to effectively estimate the sensitive attribute from the training data with relevant features in various formats and utilize the estimated sensitive attribute information to learn fair models. Experimental results on real-world datasets show the effectiveness of our framework in terms of both accuracy and fairness.