Abstract Despite abundant literature theorizing societal implications of algorithmic decision making, relatively little is known about the conditions that lead to the acceptance or rejection of algorithmically generated insights by individual users of decision aids. More specifically, recent findings of algorithm aversion—the reluctance of human forecasters to use superior but imperfect algorithms—raise questions about whether joint human‐algorithm decision making is feasible in practice. In this paper, we systematically review the topic of algorithm aversion as it appears in 61 peer‐reviewed articles between 1950 and 2018 and follow its conceptual trail across disciplines. We categorize and report on the proposed causes and solutions of algorithm aversion in five themes: expectations and expertise, decision autonomy, incentivization, cognitive compatibility, and divergent rationalities. Although each of the presented themes addresses distinct features of an algorithmic decision aid, human users of the decision aid, and/or the decision making environment, apparent interdependencies are highlighted. We conclude that resolving algorithm aversion requires an updated research program with an emphasis on theory integration. We provide a number of empirical questions that can be immediately carried forth by the behavioral decision making community.

Jason W. Burton

Mari‐Klara Stein

Tina Blegind Jensen

Journal of Behavioral Decision Making

The Journal of Behavioral Decision Making is a multidisciplinary journal with a broad base of content and style. It publishes original empirical reports, critical review papers, theoretical analyses and methodological contributions. The Journal also features book, software and decision aiding technique reviews, abstracts of important articles published elsewhere and teaching suggestions.    The objective of the Journal is to present and stimulate behavioral research on decision making and to provide a forum for the evaluation of complementary, contrasting and conflicting perspectives. These perspectives include psychology, management science, sociology, political science and economics. Studies of behavioral decision making in naturalistic and applied settings are encouraged.

The Journal of Behavioral Decision Making is a multidisciplinary journal with a wide range of content and styles. It publishes original empirical reports, critical review papers, theoretical analyses, and methodological contributions. The journal also features reviews of books, software, and decision-aiding techniques, abstracts of significant articles published elsewhere, and teaching suggestions. The objective of the journal is to present and stimulate behavioral research on decision making and to provide a forum for evaluating complementary, contrasting, and conflicting perspectives. These perspectives include psychology, management science, sociology, political science, and economics. Studies of behavioral decision making in naturalistic and applied settings are encouraged.

《Journal of Behavioral Decision Making》是一本多学科期刊，内容广泛，风格多样。期刊发表原创的实证研究报告、批判性综述论文、理论分析和方法论贡献。此外，期刊还包括书籍、软件和决策辅助技术的评审，重要文章的摘要，以及教学建议。期刊的目标是展示和激励关于决策制定的行为研究，并为评估互补、对立和冲突的观点提供一个论坛。这些观点涵盖心理学、管理科学、社会学、政治学和经济学。鼓励在自然和应用环境中进行的行为决策研究。

J Behav Dec Making

A systematic review of algorithm aversion in augmented decision making

It is concluded that resolving algorithm aversion requires an updated research program with an emphasis on theory integration, and a number of empirical questions that can be immediately carried forth by the behavioral decision making community are provided.

Category	Quartile
心理学	3
心理学, 心理学应用	4

A systematic review of algorithm aversion in augmented decision making

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Journal Info

Category	Quartile
PSYCHOLOGY, APPLIED	3