Understanding, Explaining, and Utilizing Medical Artificial Intelligence

Cadario, R., Longoni, C. & Morewedge, C.K. (2021) Understanding, explaining, and utilizing medical artificial intelligence. Nature Human Behaviour, https://doi.org/10.1038/s41562-021-01146-0

37 Pages Posted: 2 Oct 2020 Last revised: 5 Oct 2021

See all articles by Romain Cadario

Romain Cadario

Erasmus University, Rotterdam School of Management

Chiara Longoni

Boston University, Questrom School of Business

Carey Morewedge

Boston University, Questrom School of Business

Date Written: June 28, 2021

Abstract

Medical artificial intelligence is cost-effective, scalable, and often outperforms human providers. One important barrier to its adoption is the perception that algorithms are a “black box”—people do not subjectively understand how algorithms make medical decisions, and we find this impairs their utilization. We argue a second barrier is that people also overestimate their objective understanding of medical decisions made by human healthcare providers. In five pre-registered experiments with convenience and nationally representative samples (N = 2,699), we find that people exhibit such an illusory understanding of human medical decision making (Study 1). This leads people to claim greater understanding of decisions made by human than algorithmic healthcare providers (Studies 2A-B), which makes people more reluctant to utilize algorithmic providers (Studies 3A-B). Fortunately, we find that asking people to explain the mechanisms underlying medical decision making reduces this illusory gap in subjective understanding (Study 1). Moreover, we test brief interventions that, by increasing subjective understanding of algorithmic decision processes, increase willingness to utilize algorithmic healthcare providers without undermining utilization of human providers (Studies 3A-B). Corroborating these results, a study on Google testing ads for an algorithmic skin cancer detection app shows that interventions that increase subjective understanding of algorithmic decision processes lead to a higher ad click-through rate (Study 4). Our findings show how reluctance to utilize medical algorithms is driven both by the difficulty of understanding algorithms, and an illusory understanding of human decision making.

Keywords: Algorithm Aversion, Illusion of Explanatory Depth, Medical Decision Making, Healthcare

Suggested Citation

Cadario, Romain and Longoni, Chiara and Morewedge, Carey, Understanding, Explaining, and Utilizing Medical Artificial Intelligence (June 28, 2021). Cadario, R., Longoni, C. & Morewedge, C.K. (2021) Understanding, explaining, and utilizing medical artificial intelligence. Nature Human Behaviour, https://doi.org/10.1038/s41562-021-01146-0, Available at SSRN: https://ssrn.com/abstract=3675363 or http://dx.doi.org/10.2139/ssrn.3675363

Romain Cadario (Contact Author)

Erasmus University, Rotterdam School of Management ( email )

P.O. Box 1738
Room T08-21
3000 DR Rotterdam, 3000 DR
Netherlands

Chiara Longoni

Boston University, Questrom School of Business ( email )

United States

Carey Morewedge

Boston University, Questrom School of Business ( email )

595 Commonwealth Ave
614, Marketing Department
Boston, MA 02215
United States

HOME PAGE: http://careymorewedge.com

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