The Effects of Situational and Individual Factors on Algorithm Acceptance in COVID-19-Related Decision-Making: A Preregistered Online Experiment

Q1 Social Sciences
S. Utz, Lara N. Wolfers, A. Göritz
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引用次数: 5

Abstract

In times of the COVID-19 pandemic, difficult decisions such as the distribution of ventilators must be made. For many of these decisions, humans could team up with algorithms; however, people often prefer human decision-makers. We examined the role of situational (morality of the scenario; perspective) and individual factors (need for leadership; conventionalism) for algorithm preference in a preregistered online experiment with German adults (n = 1,127). As expected, algorithm preference was lowest in the most moral-laden scenario. The effect of perspective (i.e., decision-makers vs. decision targets) was only significant in the most moral scenario. Need for leadership predicted a stronger algorithm preference, whereas conventionalism was related to weaker algorithm preference. Exploratory analyses revealed that attitudes and knowledge also mattered, stressing the importance of individual factors.
情境和个体因素对新冠肺炎相关决策算法接受度的影响——一项预注册在线实验
在2019冠状病毒病大流行期间,必须做出分发呼吸机等艰难决定。对于其中的许多决策,人类可以与算法合作;然而,人们通常更喜欢人类决策者。我们考察了情境(情境道德)的作用;视角)和个人因素(领导力需求;在德国成年人(n = 1,127)中预先注册的在线实验中,传统主义)对算法偏好的影响。正如预期的那样,在道德负担最重的情况下,算法偏好最低。视角的影响(即决策者vs.决策目标)只在最道德的场景中显著。领导需要预测较强的算法偏好,而传统主义预测较弱的算法偏好。探索性分析表明,态度和知识也很重要,强调了个人因素的重要性。
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来源期刊
CiteScore
10.00
自引率
0.00%
发文量
10
审稿时长
8 weeks
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