As artificial intelligence (AI) transforms society, understanding factors that influence AI receptivity is increasingly important. The current research investigates which types of consumers have greater AI receptivity. Contrary to expectations revealed in four surveys, cross country data and six additional studies find that people with lower AI literacy are typically more receptive to AI. This lower literacy-greater receptivity link is not explained by differences in perceptions of AI’s capability, ethicality, or feared impact on humanity. Instead, this link occurs because people with lower AI literacy are more likely to perceive AI as magical and experience feelings of awe in the face of AI’s execution of tasks that seem to require uniquely human attributes. In line with this theorizing, the lower literacy-higher receptivity link is mediated by perceptions of AI as magical and is moderated among tasks not assumed to require distinctly human attributes. These findings suggest that companies may benefit from shifting their marketing efforts and product development towards consumers with lower AI literacy. Additionally, efforts to demystify AI may inadvertently reduce its appeal, indicating that maintaining an aura of magic around AI could be beneficial for adoption.

Climate change is currently one of humanity’s greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries.

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

The growing prevalence of artificial intelligence (AI) in our lives has brought the impact of AI-based decisions on human judgments to the forefront of academic scholarship and public debate. Despite growth in research on people's receptivity towards AI, little is known about how interacting with AI shapes subsequent interactions among people. We explore this question in the context of unfair decisions determined by AI versus humans and focus on the spillover effects of experiencing such decisions on the propensity to act prosocially. Four experiments (combined N = 2425) show that receiving an unfair allocation by an AI (versus a human) actor leads to lower rates of prosocial behavior towards other humans in a subsequent decision-an effect we term AI-induced indifference. In Experiment 1, after receiving an unfair monetary allocation by an AI (versus a human) actor, people were less likely to act prosocially, defined as punishing an unfair human actor at a personal cost in a subsequent, unrelated decision. Experiments 2a and 2b provide evidence for the underlying mechanism: People blame AI actors less than their human counterparts for unfair behavior, decreasing people's desire to subsequently sanction injustice by punishing the unfair actor. In an incentive-compatible design, Experiment 3 shows that AI-induced indifference manifests even when the initial unfair decision and subsequent interaction occur in different contexts. These findings illustrate the spillover effect of human-AI interaction on human-to-human interactions and suggest that interacting with unfair AI may desensitize people to the bad behavior of others, reducing their likelihood to act prosocially. Implications for future research are discussed. All preregistrations, data, code, statistical outputs, stimuli qsf files, and the Supplementary Appendix are posted on OSF at: https://bit.ly/OSF_unfairAI.

Across a range of decision contexts, we provide evidence of a novel proximity bias in probability judgments, whereby spatial distance and outcome valence systematically interact in determining probability judgments. Six hypothetical and incentive-compatible experiments (combined N = 4007) show that a positive outcome is estimated as more likely to occur when near than distant, whereas a negative outcome is estimated as less likely to occur when near than distant (studies 1-6). The proximity bias is explained by wishful thinking and thus perceptions of outcome desirability (study 3), and it does not manifest when an outcome is less relevant for the self, such as the case of outcomes with little consequence for the self (studies 4 and 5) or when estimating outcomes for others who are irrelevant to the self (study 6). Overall, the proximity bias we document deepens our understanding of the antecedents of probability judgments.

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

Artificial intelligence (AI) is pervading the government and transforming how public services are provided to consumers across policy areas spanning allocation of government benefits, law enforcement, risk monitoring, and the provision of services. Despite technological improvements, AI systems are fallible and may err. How do consumers respond when learning of AI failures? In 13 preregistered studies (N = 3,724) across a range of policy areas, the authors show that algorithmic failures are generalized more broadly than human failures. This effect is termed "algorithmic transference" as it is an inferential process that generalizes (i.e., transfers) information about one member of a group to another member of that same group. Rather than reflecting generalized algorithm aversion, algorithmic transference is rooted in social categorization: it stems from how people perceive a group of AI systems versus a group of humans. Because AI systems are perceived as more homogeneous than people, failure information about one AI algorithm is transferred to another algorithm to a greater extent than failure information about a person is transferred to another person. Capturing AI's impact on consumers and societies, these results show how the premature or mismanaged deployment of faulty AI technologies may undermine the very institutions that AI systems are meant to modernize.

The emergence of generative AI has raised unprecedented concerns about plagiarism. We present six preregistered studies demonstrating that plagiarizing material created by AI is seen as less unethical and more permissible than plagiarizing material created by a human—an AI-human unethicality gap. Students report having plagiarized more from AI than human-generated content in the past (Study 1) and indicate greater willingness to do so in their school assignments, even when ease and convenience of accessing such content are held constant (Study 2). Moreover, people judge plagiarizing AI-generated content as less unethical and more permissible than plagiarizing human-generated content and are less likely to view it as plagiarism (Study 3). Rather than being due to differences in legal ownership (Study 4), the AI-human unethicality gap is explained by psychological ownership over the copied material (Studies 4 and 5). AI is perceived as owning the content it creates to a lesser extent than humans: when using content produced by AI (vs. humans), users are afforded greater psychological ownership over the content, reducing the perceived unethicality of passing off the content as their own. Differences in psychological ownership appear to stem from ascriptions of sentience to the content creator: imbuing AI with sentience attenuates differences in perceived ownership and in turn the AI-human unethicality gap (Study 6). These findings contribute to understanding the social effects of AI, attribution of psychological ownership, and navigating plagiarism in the age of AI.

Artificial Intelligence (AI) is pervading the government and transforming how public services are provided to consumers---from allocation of benefits to law enforcement, risk monitoring and the provision of services. Despite technological improvements, AI systems are fallible and may err. How do consumers respond when learning of AI's failures? In thirteen preregistered studies (N = 3,724), we document a robust effect of algorithmic transference: algorithmic failures are generalized more broadly than human failures. Rather than reflecting generalized algorithm aversion, algorithmic transference is rooted in social categorization: it stems from how people perceive a group of AI systems versus a group of humans---as outgroups characterized by greater homogeneity than ingroups of comparable humans. Because AI systems are perceived as more homogeneous than people, failure information about one AI algorithm is transferred to another algorithm at a higher rate than failure information about a person is transferred to another person. Assessing AI's impact on consumers and societies, we show how the premature or mismanaged deployment of faulty AI technologies may engender algorithmic transference and undermine the very institutions that AI systems are meant to modernize.

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 ( N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic ( r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

Artificial Intelligence (AI) is pervading the government and transforming how public services are provided to consumers---from allocation of benefits to law enforcement, risk monitoring and the provision of services. Despite technological improvements, AI systems are fallible and may err. How do consumers respond when learning of AI's failures? In thirteen preregistered studies (N = 3,724), we document a robust effect of algorithmic transference: algorithmic failures are generalized more broadly than human failures. Rather than reflecting generalized algorithm aversion, algorithmic transference is rooted in social categorization: it stems from how people perceive a group of AI systems versus a group of humans---as outgroups characterized by greater homogeneity than ingroups of comparable humans. Because AI systems are perceived as more homogeneous than people, failure information about one AI algorithm is transferred to another algorithm at a higher rate than failure information about a person is transferred to another person. Assessing AI's impact on consumers and societies, we show how the premature or mismanaged deployment of faulty AI technologies may engender algorithmic transference and undermine the very institutions that AI systems are meant to modernize.

Marketers are adopting increasingly sophisticated ways to engage with customers throughout their journeys. We extend prior perspectives on the customer journey by introducing the role of digital signals that consumers emit throughout their activities. We argue that the ability to detect and act on consumer digital signals is a source of competitive advantage for firms. Technology enables firms to collect, interpret, and act on these signals to better manage the customer journey. While some consumers' desire for privacy can restrict the opportunities technology provides marketers, other consumers' desire for personalization can encourage the use of technology to inform marketing efforts. We posit that this difference in consumers' willingness to emit observable signals may hinge on the strength of their relationship with the firm. We next discuss factors that may shift consumer preferences and consequently affect the technology-enabled opportunities available to firms. We conclude with a research agenda that focuses on consumers, firms, and regulators.

Artificial Intelligence (AI) algorithms are now able to produce text virtually indistinguishable from text written by humans across a variety of domains. A key question, then, is whether people believe content from AI as much as content from humans. Trust in the (human generated) news media has been decreasing over time and AI is viewed as lacking human desires, and emotions, suggesting that AI news may be viewed as more accurate. Contrary to this, two preregistered experiments conducted on representative U.S. samples (combined N = 4,034) showed that people rated news produced by AI as being less accurate than news produced by humans. When news items were tagged as produced by AI (compared to a human), people were more likely to incorrectly rate them as inaccurate when they were actually true, and more likely to correctly rate them as inaccurate when they were indeed false. These results were robust to experimental paradigm (separate and joint evaluations), news item (actual veracity, age), and several respondent characteristics (e.g., political orientation). This effect is particularly important given the increasing use of AI algorithms in news production, and the associated ethical and governance pressures to disclose their use.