People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error
People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error
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DOI：
10.1177/0956797620948841
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发表时间：
2020-09-11
影响因子：
8.2
通讯作者：
Bharti, Soaham
中科院分区：
文献类型：
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作者：
Dietvorst, Berkeley J.;Bharti, Soaham
如果自动驾驶汽车、虚拟医生和其他算法决策者的表现超过人类，人们会使用它们吗？答案取决于决策域中固有的不确定性。我们提出，人们对预测错误的敏感性逐渐降低，这种偏好导致人们倾向于风险更高（通常表现更差）的决策方法，如人类判断，在固有的不确定领域。在9项研究（N= 4,820）中，我们发现(a)人们对预测产生的每个边际误差单位的敏感性逐渐降低，(b)人们不太可能在更不可预测的决策领域使用最佳算法，(c)人们根据这些方法产生接近完美答案的感知可能性在决策方法之间进行选择，(d)人们更喜欢表现出更高差异的方法（其他条件相同）。在某种程度上，投资、医疗决策和其他领域本质上是不确定的，人们可能不愿意在这些领域使用即使是最好的算法。
Will people use self-driving cars, virtual doctors, and other algorithmic decision-makers if they outperform humans? The answer depends on the uncertainty inherent in the decision domain. We propose that people have diminishing sensitivity to forecasting error and that this preference results in people favoring riskier (and often worse-performing) decision-making methods, such as human judgment, in inherently uncertain domains. In nine studies (N= 4,820), we found that (a) people have diminishing sensitivity to each marginal unit of error that a forecast produces, (b) people are less likely to use the best possible algorithm in decision domains that are more unpredictable, (c) people choose between decision-making methods on the basis of the perceived likelihood of those methods producing a near-perfect answer, and (d) people prefer methods that exhibit higher variance in performance (all else being equal). To the extent that investing, medical decision-making, and other domains are inherently uncertain, people may be unwilling to use even the best possible algorithm in those domains.