Algorithmic decision-making (ADM) increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires taking people's fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (a) algorithmic predictors, (b) human predictors, (c) comparative effects (human decision-making vs. algorithmic decision-making), and (d) consequences of ADM. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible ADM.
翻译:分析决策(ADM)日益影响人们的日常生活。鉴于这种自主系统可能对个人和社会群体造成严重伤害,因此出现了公平问题。学者和决策者所要求的以人为中心的方法要求在设计和实施ADM时考虑人们的公平观念。我们提供了全面、系统的文献审查,综合了39项涉及多个领域和科学学科的经验研究中关于算法公平观念的现有经验性见解。我们通过透彻的编码,将目前的实证文献系统化为四个方面:(a) 算法预测器,(b) 人类预测器,(c) 比较效果(人类决策相对于算法决策),以及(d) ADM的后果。虽然我们发现围绕理论概念和算法公平经验衡量方法的许多差异性,但洞察力几乎完全来自西方民主背景。通过倡导采用社会内部框架进行更跨学科的研究,我们希望我们的工作将有助于更公平和更负责的ADMDM。