Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. In this work, we conduct a human subject study to assess people's perceptions of informational fairness (i.e., whether people think they are given adequate information on and explanation of the process and its outcomes) and trustworthiness of an underlying ADS when provided with varying types of information about the system. More specifically, we instantiate an ADS in the area of automated loan approval and generate different explanations that are commonly used in the literature. We randomize the amount of information that study participants get to see by providing certain groups of people with the same explanations as others plus additional explanations. From our quantitative analyses, we observe that different amounts of information as well as people's (self-assessed) AI literacy significantly influence the perceived informational fairness, which, in turn, positively relates to perceived trustworthiness of the ADS. A comprehensive analysis of qualitative feedback sheds light on people's desiderata for explanations, among which are (i) consistency (both with people's expectations and across different explanations), (ii) disclosure of monotonic relationships between features and outcome, and (iii) actionability of recommendations.
翻译:自动决策系统(ADS)越来越多地用于相应的决策,这些系统往往依赖复杂而不透明的机器学习模式,无法理解某项决定是如何作出的。在这项工作中,我们开展了一项人文主题研究,以评估人们对信息公平(即人们是否认为他们得到了关于程序及其结果的充分信息和解释)的看法,以及基本ADS在获得关于系统的各种信息时是否可信。更具体地说,我们在自动贷款批准领域即时启用ADS,并生成文献中常用的不同解释。我们随机地将研究参与者所看到的信息量从向某些人群提供与其他人相同的解释,并附加解释。我们从定量分析中发现,不同数量的信息以及人们(自评的)AI识字极大地影响人们所认为的信息公平性,而这反过来又与ADS的可信任性有关。对质量反馈的全面分析揭示了人们的解释的不相容性,其中(i)是(i)在人们的期望和不同行动之间的一致性(i)以及(i)结果解释(i)和结果(i)之间的一致性(i)和结果(i)和结果(i)之间的一致性关系。