The lesser of two evils: Assessing the public acceptance of AI thermal facial recognition during the COVID‐19 crisis

Abstract AI thermal facial recognition (AITFR) has been rapidly applied globally in the fight against Coronavirus disease 2019 (COVID‐19). However, AITFR has also been accompanied by a controversy regarding whether the public accepts it. Therefore, it is necessary to assess the acceptance of AITFR during the COVID‐19 crisis. Drawing upon the theory of acceptable risk and Siegrist's causal model of public acceptance (PA), we built a combined psychological model that included the perceived severity of COVID‐19 (PSC) to describe the influencing factors and pathways of AITFR acceptance. This model was verified through a survey conducted in Xi'an City, Shaanxi Province, China, which collected 754 valid questionnaires. The results show that (1) COVID‐19 provides various application scenarios for AI‐related technologies. However, the respondents’ trust in AITFR was found to be very low. Additionally, the public appeared concerned about the privacy disclosure issue and the accuracy of the AITFR algorithm. (2) The PSC, social trust (ST), and perceived benefit (PB) were found to directly affect AITFR acceptance. (3) The PSC was found to have a significant positive effect on perceived risk (PR). PR was found to have no significant effect on PA, which is inconsistent with the findings of previous studies. (4) The PB were found to be a stronger mediator of the indirect effect of the PSC on ST induced by AITFR acceptance.