A growing number of oversight boards and regulatory bodies seek to monitor and govern algorithms that make decisions about people's lives. Prior work has explored how people believe algorithmic decisions should be made, but there is little understanding of how individual factors like sociodemographics or direct experience with a decision-making scenario may affect their ethical views. We take a step toward filling this gap by exploring how people's perceptions of one aspect of procedural algorithmic fairness (the fairness of using particular features in an algorithmic decision) relate to their (i) demographics (age, education, gender, race, political views) and (ii) personal experiences with the algorithmic decision-making scenario. We find that political views and personal experience with the algorithmic decision context significantly influence perceptions about the fairness of using different features for bail decision-making. Drawing on our results, we discuss the implications for stakeholder engagement and algorithmic oversight including the need to consider multiple dimensions of diversity in composing oversight and regulatory bodies.
翻译:越来越多的监督委员会和监管机构寻求监测和管理对人的生活作出决定的算法; 先前的工作探索了人们如何相信算法决定应该如何作出,但对于诸如社会人口学或决策情景直接经验等个别因素会如何影响其道德观点却知之甚少; 我们为填补这一空白迈出了一步,探索人们对于程序算法公平(在算法决定中使用特定特征的公平性)的一个方面的看法如何与其(一) 人口统计(年龄、教育、性别、种族、政治观点)和(二) 算法决策情景的个人经验有关; 我们发现,在算法决策背景下的政治观点和个人经验对使用不同特征进行保释决策的公平性影响很大; 根据我们的成果,我们讨论了对利益攸关方参与和算法监督的影响,包括需要考虑在组成监督和监管机构时的多样性的多重层面。