相关性(法律)
计算机科学
对象(语法)
算法
最大化
领域(数学分析)
结果(博弈论)
人工智能
机器学习
经济
心理学
社会心理学
微观经济学
数学
法学
政治学
数学分析
作者
Berkeley J. Dietvorst,Daniel M. Bartels
摘要
Why do consumers embrace some algorithms and find others objectionable? The moral relevance of the domain in which an algorithm operates plays a role. The authors find that consumers believe that algorithms are more likely to use maximization (i.e., attempting to maximize some measured outcome) as a decision‐making strategy than human decision makers (Study 1). Consumers find this consequentialist decision strategy to be objectionable in morally relevant tradeoffs and disapprove of algorithms making morally relevant tradeoffs as a result (Studies 2, 3a, & 3b). Consumers also object to human employees making morally relevant tradeoffs when they are trained to make decisions by maximizing outcomes, consistent with the notion that their objections to algorithmic decision makers stem from concerns about maximization (Study 4). The results provide insight into why consumers object to some consumer relevant algorithms while adopting others.
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