算法公平与社会福利
Algorithmic Fairness and Social Welfare
AI总结：
本文梳理算法公平的统计群体准则与基于“无知之幕”的社会福利公平观之间的关系，考察前者能否由幕布后的选择推出及其区分性质。
摘要：
算法正越来越多地被用于指导有关个人的高风险决策。因此，围绕定义和衡量这些算法的“公平性”，人们产生了浓厚兴趣。这些关于公平算法的定义具有两个共同特征：第一，它们通过关注算法影响在不同群体之间如何系统性地存在差异，优先考虑预先定义的群体身份（例如种族或性别）的作用。第二，它们本质上是统计性的；例如，比较假阳性率，或评估群体身份是否独立于决策（二者都被视为随机变量）。这些概念在表面上不同于研究公平的社会福利方法，尤其是不同于基于“无知之幕”思想实验的方法；在该实验中，个人在其社会身份实现之前选择如何构建社会。在本文中，我们试图理解并梳理这些不同公平研究方法之间的关系。算法公平文献中提出的优化准则，是否也可以被解释为处于无知之幕背后的某人所作出的选择？如果不能，又是哪些性质区分了这两种公平研究方法？
英文摘要：
Algorithms are increasingly used to guide high-stakes decisions about individuals. Consequently, substantial interest has developed around defining and measuring the ``fairness'' of these algorithms. These definitions of fair algorithms share two features: First, they prioritize the role of a pre-defined group identity (e.g., race or gender) by focusing on how the algorithm's impact differs systematically across groups. Second, they are statistical in nature; for example, comparing false positive rates, or assessing whether group identity is independent of the decision (where both are viewed as random variables). These notions are facially distinct from a social welfare approach to fairness, in particular one based on ``veil of ignorance'' thought experiments in which individuals choose how to structure society prior to the realization of their social identity. In this paper, we seek to understand and organize the relationship between these different approaches to fairness. Can the optimization criteria proposed in the algorithmic fairness literature also be motivated as the choices of someone from behind the veil of ignorance? If not, what properties distinguish either approach to fairness?
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
