Learning fair models without sensitive attributes: A generative approach
Learning fair models without sensitive attributes: A generative approach
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DOI：
10.1016/j.neucom.2023.126841
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发表时间：
2023-09
期刊：
影响因子：
6
通讯作者：
Huaisheng Zhu;Enyan Dai;Hui Liu;Suhang Wang
中科院分区：
文献类型：
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作者：
Huaisheng Zhu;Enyan Dai;Hui Liu;Suhang Wang
现有的公平分类器大多依赖于敏感属性来实现公平性。但是，对于许多场景，由于隐私和法律的问题，我们无法获得敏感属性。缺乏敏感属性的挑战，许多现有的公平分类。虽然我们缺乏敏感属性，但对于许多应用程序来说，通常存在与敏感属性相关的各种格式的特征/信息。例如，一个人的购买历史可以反映他/她的种族，这将有助于学习关于种族的公平分类器。然而，探索学习公平的模型，没有敏感属性的相关功能的工作是相当有限的。因此，在本文中，我们研究了一个新的问题，学习公平的模型不敏感的属性，通过探索相关的功能。我们提出了一个概率生成框架，以有效地估计敏感属性的训练数据的相关功能，在各种格式，并利用估计的敏感属性信息学习公平的模型。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的有效性。我们的源代码可以在https://github.com/huaishengzhu/FairWS上找到。
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/information of various formats that are relevant to sensitive attributes. For example, a person’s purchase history can reflect his/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. Our source code is available at: https://github.com/huaishengzhu/FairWS.