论文标题
Sofair：单镜头公平代表性学习
SoFaiR: Single Shot Fair Representation Learning
论文作者
论文摘要
为了避免对数据的歧视用途，组织可以学会将其映射到一个表示与敏感属性相关的信息的表示形式中。但是，公平代表学习中的所有现有方法都会产生公平的信息权衡。为了在公平信息平面上实现不同的观点，必须训练不同的模型。在本文中，我们首先证明了公平信息的权衡是完全由利率降级权衡取舍的。然后，我们使用此关键结果，并提出Sofair，这是一种单镜头公平表示学习方法，该方法在公平信息传播平面上以一个训练的模型生成许多点。除了其计算节省外，我们的单发方法在我们的知识范围内，是第一个公平表示方法，该方法解释了哪些信息受代表的公平 /失真属性的变化影响。从经验上讲，我们在三个数据集中发现，SOFAIR实现了与多拍的相似的公平信息权衡取舍。
To avoid discriminatory uses of their data, organizations can learn to map them into a representation that filters out information related to sensitive attributes. However, all existing methods in fair representation learning generate a fairness-information trade-off. To achieve different points on the fairness-information plane, one must train different models. In this paper, we first demonstrate that fairness-information trade-offs are fully characterized by rate-distortion trade-offs. Then, we use this key result and propose SoFaiR, a single shot fair representation learning method that generates with one trained model many points on the fairness-information plane. Besides its computational saving, our single-shot approach is, to the extent of our knowledge, the first fair representation learning method that explains what information is affected by changes in the fairness / distortion properties of the representation. Empirically, we find on three datasets that SoFaiR achieves similar fairness-information trade-offs as its multi-shot counterparts.
