论文标题
公平的投入和公平输出：隐私和准确性公平性的不相容性
Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy
论文作者
论文摘要
对算法决策系统的公平关注主要集中在输出上（例如，分类器跨个体或组的准确性）。但是，可能还与输入中的公平性有关。在本文中，我们提出并制定了有关分类器（使用）的输入的两种属性。特别是，我们声称公平的隐私（是否都要求个人透露相同的信息）和需要知道的（是否要求用户是否只要求用户手头任务所需的最小信息）是决策系统的理想属性。我们探讨了这些属性与输出中公平性之间的相互作用（公平预测的准确性）。我们表明，对于最佳分类器，这三个属性通常是不兼容的，我们说明数据的共同属性使它们不兼容。最后，我们提供了一种算法来验证给定数据集中三个属性之间的权衡是否存在，并使用算法表明这种权衡在实际数据中很常见。
Fairness concerns about algorithmic decision-making systems have been mainly focused on the outputs (e.g., the accuracy of a classifier across individuals or groups). However, one may additionally be concerned with fairness in the inputs. In this paper, we propose and formulate two properties regarding the inputs of (features used by) a classifier. In particular, we claim that fair privacy (whether individuals are all asked to reveal the same information) and need-to-know (whether users are only asked for the minimal information required for the task at hand) are desirable properties of a decision system. We explore the interaction between these properties and fairness in the outputs (fair prediction accuracy). We show that for an optimal classifier these three properties are in general incompatible, and we explain what common properties of data make them incompatible. Finally we provide an algorithm to verify if the trade-off between the three properties exists in a given dataset, and use the algorithm to show that this trade-off is common in real data.
