People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error

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 like human judgment in inherently uncertain domains. In nine studies (N=4,820), we find that: people have diminishing sensitivity to forecasting error, people are less likely to use the best possible algorithm in decision domains that are more unpredictable, people choose between decision-making methods based on their perceived likelihood of those methods producing a near perfect answer, and 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