Understanding, explaining, and utilizing medical artificial intelligence

Romain Cadario (), Chiara Longoni and Carey K. Morewedge
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Romain Cadario: Erasmus University
Chiara Longoni: Boston University
Carey K. Morewedge: Boston University

Nature Human Behaviour, 2021, vol. 5, issue 12, 1636-1642

Abstract: Abstract Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artificial intelligence is driven by both the subjective difficulty of understanding algorithms (the perception that they are a ‘black box’) and by an illusory subjective understanding of human medical decision-making. In five pre-registered experiments (1–3B: N = 2,699), we find that people exhibit an illusory understanding of human medical decision-making (study 1). This leads people to believe they better understand decisions made by human than algorithmic healthcare providers (studies 2A,B), which makes them more reluctant to utilize algorithmic than human providers (studies 3A,B). Fortunately, brief interventions that increase subjective understanding of algorithmic decision processes increase willingness to utilize algorithmic healthcare providers (studies 3A,B). A sixth study on Google Ads for an algorithmic skin cancer detection app finds that the effectiveness of such interventions generalizes to field settings (study 4: N = 14,013).

Date: 2021
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DOI: 10.1038/s41562-021-01146-0

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