The COVID-19 pandemic continues to impact people worldwide–steadily depleting scarce resources in healthcare. Medical Artificial Intelligence (AI) promises a much-needed relief but only if the technology gets adopted at scale. The present research investigates people’s intention to adopt medical AI as well as the drivers of this adoption in a representative study of two European countries (Denmark and France, N = 1068) during the initial phase of the COVID-19 pandemic. Results reveal AI aversion; only 1 of 10 individuals choose medical AI over human physicians in a hypothetical triage-phase of COVID-19 pre-hospital entrance. Key predictors of medical AI adoption are people’s trust in medical AI and, to a lesser extent, the trait of open-mindedness. More importantly, our results reveal that mistrust and perceived uniqueness neglect from human physicians, as well as a lack of social belonging significantly increase people’s medical AI adoption. These results suggest that for medical AI to be widely adopted, people may need to express less confidence in human physicians and to even feel disconnected from humanity. We discuss the social implications of these findings and propose that successful medical AI adoption policy should focus on trust building measures–without eroding trust in human physicians.

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How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data.

Experiences of financial scarcity (i.e., perceptions of “having less than needed”) can distort decision-making, capture attention, and make individuals risk-seeking and short-term oriented. However, the influence of scarcity on information acquisition and ethical decision-making remains poorly understood. This eye-tracking study explored how acute financial scarcity affects ethical decision-making and shapes selective information search in an economic task with competing incentives (N = 60). Contrary to predictions, participants experiencing scarcity were less likely to cheat for economic gains, indicating that scarcity does not necessarily reduce ethical behavior. Participants displayed a strong attentional bias towards high-paying choices but did not act unethically. These findings might reveal a "moral boundary" dictating when attentional biases translate into decision-making. Our results contribute to understanding how individuals in scarcity contexts process and prioritize information in ethical decision-making, helping organizations and policymakers combat stereotypes surrounding resource-deprived individuals, and design evidence-based policy interventions promoting ethical behavior in financially scarce situations.

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