Machine learning models have demonstrated promising performance in many areas. However, the concerns that they can be biased against specific demographic groups hinder their adoption in high-stake applications. Thus, it is essential to ensure fairness in machine learning models. Most previous efforts require direct access to sensitive attributes for mitigating bias. Nonetheless, it is often infeasible to obtain large-scale users' sensitive attributes considering users' concerns about privacy in the data collection process. Privacy mechanisms such as local differential privacy (LDP) are widely enforced on sensitive information in the data collection stage due to legal compliance and people's increasing awareness of privacy. Therefore, a critical problem is how to make fair predictions under privacy. We study a novel and practical 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 achieve Fair prediction under the Semi-Private setting. First, FairSP learns to correct the noise-protected sensitive attributes by exploiting the limited clean sensitive attributes. 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 under mild assumptions in the semi-private setting. Extensive experimental results on real-world datasets demonstrate the effectiveness of our method for making fair predictions under privacy and maintaining high accuracy.

翻译：机器学习模型在许多领域展现出令人瞩目的性能。然而，它们可能对特定人群产生偏见的担忧，阻碍了其在高风险应用中的推广。因此，确保机器学习模型的公平性至关重要。以往大多数研究需要直接访问敏感属性以减轻偏见。然而，考虑到用户在数据收集过程中对隐私的担忧，获取大规模用户的敏感属性往往不可行。由于法律合规要求以及人们对隐私意识的日益增强，本地差分隐私（LDP）等隐私机制在数据收集阶段被广泛应用于敏感信息保护。因此，一个关键问题是如何在隐私保护下实现公平预测。我们研究了半私有设置下的公平分类这一新颖且实际的问题，在该设置中，大多数敏感属性是私有的，仅有一小部分干净的敏感属性可用。为此，我们提出了一种新颖框架FairSP，能够在半私有设置下实现公平预测。首先，FairSP通过利用有限的干净敏感属性，学习纠正受噪声保护的敏感属性。随后，它以对抗方式联合建模纠正后数据和干净数据，用于去偏和预测。理论分析表明，所提模型在半私有设置下的温和假设中能够确保公平性。在真实数据集上的大量实验结果表明，我们的方法能有效在隐私保护下做出公平预测并保持高准确率。