When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes

Machine learning models have demonstrated promising performances in many areas. However, the concerns that they can be biased against specific groups hinder their adoption in high-stake applications. Thus, it is essential to ensure fairness in machine learning models. Most of the previous efforts require access to sensitive attributes for mitigating bias. Nevertheless, it is often infeasible to obtain a large scale of data with sensitive attributes due to people's increasing awareness of privacy and the legal compliance. Therefore, an important research question is how to make fair predictions under privacy. In this paper, we study a novel problem of fair classification in a semi-private setting, where most of the sensitive attributes are private and only a small amount of clean ones are available. To this end, we propose a novel framework FairSP that can first learn to correct the noisy sensitive attributes under the privacy guarantee by exploiting the limited clean ones. Then, it jointly models the corrected and clean data in an adversarial way for debiasing and prediction. Theoretical analysis shows that the proposed model can ensure fairness when most sensitive attributes are private. Extensive experimental results in real-world datasets demonstrate the effectiveness of the proposed model for making fair predictions under privacy and maintaining high accuracy.