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.

翻译：现有的大多数公平分类器依赖于敏感属性来实现公平性。然而，在许多场景中，由于隐私和法律问题，我们无法获取敏感属性。敏感属性的缺失对许多现有的公平分类器构成了挑战。尽管我们缺乏敏感属性，但在许多应用中，通常存在与敏感属性相关的各种格式的特征或信息。例如，个人的购买历史可以反映其种族，这有助于学习关于种族的公平分类器。然而，探索相关特征以在无敏感属性情况下学习公平模型的研究相当有限。因此，在本文中，我们研究了一个新颖的问题：通过探索相关特征，在无需敏感属性的情况下学习公平模型。我们提出了一个概率生成框架，能够有效地从包含各种格式相关特征的训练数据中估计敏感属性，并利用估计的敏感属性信息来学习公平模型。在真实世界数据集上的实验结果表明，我们的框架在准确性和公平性方面均表现出有效性。