Fair classification and social welfare
Fair classification and social welfare
复制标题
公平分类和社会福利
DOI：
10.1145/3351095.3372857
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Yiling Chen
中科院分区：
文献类型：
--
作者：
Lily Hu;Yiling Chen
既然机器学习算法处于许多重要资源分配渠道的中心，计算机科学家就不知不觉地被塑造成部分社会规划者。鉴于这种情况，重要的问题随之而来。计算机科学家定义的主要公平概念如何映射到长期存在的社会福利概念？在本文中，我们对公平分类制度进行了基于福利的分析。我们的主要研究结果评估了公平约束的经验风险最小化计划对受其产出影响的个人和群体的福利影响。我们充分描述了公平软边际 SVM 问题中公平性参数 'e 的 Δ'e 扰动范围，该问题对个人和群体的效用产生更好、更差和中性的结果。我们的分析方法可以快速有效地计算“公平到福利”的解决方案路径，从而使从业者能够轻松评估是否以及哪些公平学习程序会产生使群体变得更好的分类结果。我们的分析表明，应用更严格的公平标准（编入平等约束）可能会恶化两个群体的福利结果。更一般地说，总是偏爱“更公平”的分类器并不遵守帕累托原则——社会选择理论和福利经济学的基本公理。最近的机器学习工作集中在这些公平概念上，这对于确保算法系统不会对弱势社会群体产生不同的负面影响至关重要。通过表明这些限制往往无法转化为这些群体的改善结果，我们对它们作为确保公平和正义的手段的有效性表示怀疑。
Now that machine learning algorithms lie at the center of many important resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. How do leading notions of fairness as defined by computer scientists map onto longer-standing notions of social welfare? In this paper, we present a welfare-based analysis of fair classification regimes. Our main findings assess the welfare impact of fairness-constrained empirical risk minimization programs on the individuals and groups who are subject to their outputs. We fully characterize the ranges of Δ'e perturbations to a fairness parameter 'e in a fair Soft Margin SVM problem that yield better, worse, and neutral outcomes in utility for individuals and by extension, groups. Our method of analysis allows for fast and efficient computation of "fairness-to-welfare" solution paths, thereby allowing practitioners to easily assess whether and which fair learning procedures result in classification outcomes that make groups better-off. Our analyses show that applying stricter fairness criteria codified as parity constraints can worsen welfare outcomes for both groups. More generally, always preferring "more fair" classifiers does not abide by the Pareto Principle---a fundamental axiom of social choice theory and welfare economics. Recent work in machine learning has rallied around these notions of fairness as critical to ensuring that algorithmic systems do not have disparate negative impact on disadvantaged social groups. By showing that these constraints often fail to translate into improved outcomes for these groups, we cast doubt on their effectiveness as a means to ensure fairness and justice.