论文标题
旨在审核无监督的学习算法和公平性的人类流程
Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness
论文作者
论文摘要
关于公平性的现有工作通常集中于使已知的机器学习算法更公平。存在分类，聚类，离群检测和其他算法样式的公平变体。但是，研究的领域是审核算法的输出以确定公平性的话题。现有工作探索了使用统计奇偶校验的标准定义二进制保护状态变量的两个组分类问题。在这里，我们通过在更复杂的公平定义下探索多组设置来建立审核领域。
Existing work on fairness typically focuses on making known machine learning algorithms fairer. Fair variants of classification, clustering, outlier detection and other styles of algorithms exist. However, an understudied area is the topic of auditing an algorithm's output to determine fairness. Existing work has explored the two group classification problem for binary protected status variables using standard definitions of statistical parity. Here we build upon the area of auditing by exploring the multi-group setting under more complex definitions of fairness.
