论文标题
模型评估期间公平性的可解释评估
Interpretable Assessment of Fairness During Model Evaluation
论文作者
论文摘要
对于开发产品或算法的公司，重要的是要了解全球的潜在影响，而且了解用户的子选集。特别是，重要的是要检测某些与其他用户相比，与其他用户相比，在业务指标方面的影响有所不同，或者模型在公平性问题上对他们不平等地对待。在本文中，我们介绍了一种新型的层次聚类算法，以在给定的一组子人群中检测用户之间的异质性，相对于任何指定的组相似性概念。我们证明了有关输出的统计保证，并提供了可解释的结果。我们在LinkedIn的真实数据上演示了该算法的性能。
For companies developing products or algorithms, it is important to understand the potential effects not only globally, but also on sub-populations of users. In particular, it is important to detect if there are certain groups of users that are impacted differently compared to others with regard to business metrics or for whom a model treats unequally along fairness concerns. In this paper, we introduce a novel hierarchical clustering algorithm to detect heterogeneity among users in given sets of sub-populations with respect to any specified notion of group similarity. We prove statistical guarantees about the output and provide interpretable results. We demonstrate the performance of the algorithm on real data from LinkedIn.
