Numbers, Not Lives: AI Dehumanization Undermines COVID-19 Preventive Intentions

Li Huang, Zhi Lu and Priyali Rajagopal

Journal of the Association for Consumer Research, 2022, vol. 7, issue 1, 63 - 71

Abstract: Across four studies, we document a novel and unexpected effect such that COVID-19 data gathered by artificial intelligence (AI vs. humans) reduces consumers’ intentions to take preventive measures. This effect is driven by greater perceived dehumanization, such that consumers view AI-generated data as numbers, rather than as human lives. We find that this effect is mitigated when humanness is primed, is moderated by AI type (narrow AI leads to greater dehumanization than general AI), and is attenuated by task type (perceived vaccine effectiveness is similar for AI and humans when the vaccine development process highlights AI-advantageous attributes).

Date: 2022
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