教你在推荐系统中同时考虑多重身份交叉，避免算法加剧歧视
A Tutorial On Intersectionality in Fair Rankings
- 将种族、性别等多重身份的交互影响纳入公平排序设计
- 指出仅关注单一属性公平无法解决深层不平等问题
- 适合关注算法公平性与社会影响的研究者和工程师
我们探讨了算法偏见与不公平排名这一关键问题，其已渗透至搜索引擎、推荐系统及人力资源管理等多个领域。在数据驱动的世界中，这些偏见可能对边缘化和代表性不足群体造成歧视性后果。负责任的数据科学与人工智能努力旨在缓解此类偏见，促进公平、多样性和透明度。然而，现有公平排序方法大多仅关注种族、性别或社会经济地位等单一受保护属性，忽视了这些属性之间的交叉性——即多重社会身份的相互作用。理解交叉性对于确保公平排序不会延续既有不平等至关重要。本文通过实际案例说明如何在公平排序系统中融入交叉性，并对现有文献进行比较分析，提供一张综览表总结各类方法。我们的分析强调，实现公平需要考虑交叉性，但公平本身并不自动包含交叉性。
原文摘要 · Abstract (English)
We address the critical issue of biased algorithms and unfair rankings, which have permeated various sectors, including search engines, recommendation systems, and workforce management. These biases can lead to discriminatory outcomes in a data-driven world, especially against marginalized and underrepresented groups. Efforts towards responsible data science and responsible artificial intelligence aim to mitigate these biases and promote fairness, diversity, and transparency. However, most fairness-aware ranking methods singularly focus on protected attributes such as race, gender, or socio-economic status, neglecting the intersectionality of these attributes, i.e., the interplay between multiple social identities. Understanding intersectionality is crucial to ensure that existing inequalities are not preserved by fair rankings. We offer a description of the main ways to incorporate intersectionality in fair ranking systems through practical examples and provide a comparative overview of existing literature and a synoptic table summarizing the various methodologies. Our analysis highlights the need for intersectionality to attain fairness, while also emphasizing that fairness, alone, does not necessarily imply intersectionality.
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