论文标题
重新审视个人公平：从对抗性鲁棒性转移技术
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness
论文作者
论文摘要
我们将个人公平性的定义转向其头脑---而不是确定给定预定度量的模型的公平性，而是为给定模型的指标找到满足个人公平性的指标。这可以促进关于模型公平性的讨论，解决可能很难先验一个合适的指标的问题。我们的贡献是双重的：首先，我们介绍了最小度量的定义，并以最小指标来表征模型的行为。其次，对于更复杂的模型，我们应用了从对抗性鲁棒性中随机平滑的机制，以使它们在给定的加权$ l^p $公制下单独公平。我们的实验表明，将线性模型的最小指标适应更复杂的神经网络可以导致有意义和可解释的公平性保证，而实用程序的成本很小。
We turn the definition of individual fairness on its head---rather than ascertaining the fairness of a model given a predetermined metric, we find a metric for a given model that satisfies individual fairness. This can facilitate the discussion on the fairness of a model, addressing the issue that it may be difficult to specify a priori a suitable metric. Our contributions are twofold: First, we introduce the definition of a minimal metric and characterize the behavior of models in terms of minimal metrics. Second, for more complicated models, we apply the mechanism of randomized smoothing from adversarial robustness to make them individually fair under a given weighted $L^p$ metric. Our experiments show that adapting the minimal metrics of linear models to more complicated neural networks can lead to meaningful and interpretable fairness guarantees at little cost to utility.
