Algorithmic fairness research has traditionally been linked to the disciplines of philosophy, ethics, and economics, where notions of fairness are prescriptive and seek objectivity. Increasingly, however, scholars are turning to the study of what different people perceive to be fair, and how these perceptions can or should help to shape the design of machine learning, particularly in the policy realm. The present work experimentally explores five novel research questions at the intersection of the "Who," "What," and "How" of fairness perceptions. Specifically, we present the results of a multi-factor conjoint analysis study that quantifies the effects of the specific context in which a question is asked, the framing of the given question, and who is answering it. Our results broadly suggest that the "Who" and "What," at least, matter in ways that are 1) not easily explained by any one theoretical perspective, 2) have critical implications for how perceptions of fairness should be measured and/or integrated into algorithmic decision-making systems.

翻译：分析公平性研究传统上与哲学、伦理学和经济学学科相关,公平性概念是指令性的,寻求客观性。然而,学者们越来越多地转向研究不同的人认为什么是公平的,以及这些认识如何能或应该帮助设计机器学习的设计,特别是在政策领域。目前的工作实验性地探索了“谁”、“什么”和“如何”公平感交汇处的五个新的研究问题。具体地说,我们介绍了一个多因素联合分析研究的结果,它量化了提出问题的具体背景的影响,设定了问题的范围,以及回答问题的人。我们的结果广泛表明,“谁”和“什么”至少以一个理论角度不易解释的方式“谁”和“什么”,对于如何衡量和(或)将公平性概念纳入算法决策系统具有关键影响。