论文标题
公平排名的因果交叉口
Causal intersectionality for fair ranking
论文作者
论文摘要
在本文中，我们提出了一种因果建模方法，以实现相交的公平性，并提出了一种计算交叉上公平排名的灵活，特定于任务的方法。从网络搜索结果到大学录取范围，在许多情况下使用排名，但是公平排名的因果推断受到了有限的关注。此外，关于因果公平性的越来越多的文献对交叉的关注很少。通过在正式的因果框架中将这些问题汇总在一起，我们可以在公平的机器学习中应用交叉性，与重要的现实世界效应和领域知识相关，并透明了技术限制。我们在实验中评估了对真实和合成数据集的方法，并在不同的结构假设下探索其行为。
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used in many contexts, ranging from Web search results to college admissions, but causal inference for fair rankings has received limited attention. Additionally, the growing literature on causal fairness has directed little attention to intersectionality. By bringing these issues together in a formal causal framework we make the application of intersectionality in fair machine learning explicit, connected to important real world effects and domain knowledge, and transparent about technical limitations. We experimentally evaluate our approach on real and synthetic datasets, exploring its behaviour under different structural assumptions.
