Machine Learning or Artificial Intelligence algorithms have gained considerable scrutiny in recent times owing to their propensity towards imitating and amplifying existing prejudices in society. This has led to a niche but growing body of work that identifies and attempts to fix these biases. A first step towards making these algorithms more fair is designing metrics that measure unfairness. Most existing work in this field deals with either a binary view of fairness (protected vs. unprotected groups) or politically defined categories (race or gender). Such categorization misses the important nuance of intersectionality - biases can often be amplified in subgroups that combine membership from different categories, especially if such a subgroup is particularly underrepresented in historical platforms of opportunity. In this paper, we discuss why fairness metrics need to be looked at under the lens of intersectionality, identify existing work in intersectional fairness, suggest a simple worst case comparison method to expand the definitions of existing group fairness metrics to incorporate intersectionality, and finally conclude with the social, legal and political framework to handle intersectional fairness in the modern context.
翻译:近些年来,机器学习或人工智能算法由于倾向于模仿和扩大社会上现有的偏见而赢得了相当多的仔细审查。这导致了一整堆独特但不断增长的工作,找出并试图纠正这些偏见。使这些算法更加公平的第一步是设计衡量不公平的衡量标准。这一领域的现有工作大多涉及公平(受保护群体与无保护群体之比)或政治界定类别(种族或性别)的二进制观点。这种分类忽略了交叉性的重要特点。不同类别的成员组成的分组往往会扩大偏见,特别是如果这样一个分组在历史机会平台上代表人数特别不足。我们在本文件中讨论了为什么公平衡量标准需要从交叉性的角度来看待,找出交叉性的现有工作,提出一种简单、最糟糕的比较方法,以扩大现有群体公平衡量标准的定义,将交叉性纳入其中,最后是社会、法律和政治框架,以处理现代背景下的交叉性公平性。