Aug 2026· SN Computer Science· Vol 7· 0 citations· 79 references
TL;DR
In both cultural groups, higher perceived well-being was consistently associated with greater acceptance of AI, while ethical concerns were associated with increased fear of AI.
Abstract
As artificial intelligence (AI) systems become increasingly prevalent in decision-making, it’s essential to understand public perceptions in various contexts and cultures for responsible use. This study examines the relationship between acceptance and fear of AI, considering factors such as AI autonomy, task criticality, personality traits, need for cognition (NFC), faith in intuition (FI), locus of control (LOC), and cultural background (UK vs. Arab countries). Using the IMPACT framework, we examined the interplay between AI design modalities (automation and criticality) and individual differences in relation to perceived well-being and ethical concerns. A total of 639 participants (316 UK and 323 Arab) took part in an online survey that used scenarios to describe four types of AI: low automation/low criticality (LL), high automation/low criticality (HL), low automation/high criticality (LH), and high automation/high criticality (HH). In both cultural groups, higher perceived well-being was consistently associated with greater acceptance of AI, while ethical concerns were associated with increased fear of AI. UK participants reported the lowest well-being in the LH design, while Arab participants rated their well-being higher in both LH and HH, indicating a greater acceptance of AI autonomy in important situations. Ethical concerns were highest in the HH design for both groups. Psychological traits had some effects that varied by design. Agreeableness predicted higher well-being in both samples, while other traits, such as extraversion, openness, neuroticism, NFC, FI, and LOC, had smaller or situation-dependent effects. In the LH design, NFC and internal LOC were important predictors of well-being and ethical evaluations, especially among Arab participants. Overall, attitudes toward AI were mainly influenced by design and cultural context, while cognitive and control-related traits showed more specific influences.
The integration of artificial intelligence (AI) into decision-making processes has transformed high-stakes industries such as healthcare and the military, raising critical questions about its impact on decision-making traits such as empathy, responsibility, critical thinking, risk aversion, and self-confidence. While prior research has largely examined AI’s effects on individual traits in isolation, a gap remains in understanding how these traits interact in a complex interplay of drivers. This study aims to identify the underlying drivers by which AI-supported decision-making reshapes decision-making traits in high-stakes industries. Methodologically, the work draws on 22 semi-structured interviews with healthcare and military professionals. Based on thematic analysis, we identified five First-Order Drivers associated with trait displacement—physical and emotional distance, abstraction, perceived inferiority, added entity, and scapegoating tendency. We further identified three Second-Order Amplifiers—technological, individual, and situational factors—which intensify the magnitude of the effects. Together, these insights are synthesized in the AI-Enhanced Trait Modulation (AETM) model, an integrative framework explaining decision-making trait displacement. To situate AI’s effects in relation to other technologies, we introduce a complementary heuristic, the 5S framework. Our results highlight the growing need for ethical governance, user-centric AI design, and organizational contexts that foster critical engagement in response to AI-induced trait displacement. Especially amid accelerating AI adoption and geopolitical tension, preserving the human core of decision-making remains an ethical imperative.
L. Krøyer, Christoph Lütge· AI and Ethics· 0 citations
The ongoing proliferation of AI technologies has created a need to understand and model how people relate to, accept, and feel about AI. Among the research traditions that attempt to capture how individuals navigate these changes are those centred on the concepts of “AI acceptance” and “AI anxiety”. This perspective paper suggests that the typical use of these concepts often rests on instrumentalist perspectives of technology that are especially problematic in the context of complex sociotechnical transformations and ethical AI adoption. Firstly, AI acceptance, when based on the Technology Acceptance Model (TAM), presupposes technology acceptance as inherently rational and positive and focuses disproportionately on overcoming barriers to use, which limits its capacity to capture how people relate to AI. Secondly, the concept of AI anxiety has been used to frame critical and hesitant perspectives of AI as nonrational. By comparing these concepts and analysing their historically instrumentalist underpinnings, it is argued that both research currents require further theoretical examination and critical engagement. As contextual examples, I examine AI anxiety and acceptance as guiding concepts in select contexts, mainly education. I argue that research should engage with AI adoption as sociotechnical change where ethics, uncertainty, rationality and nonrationality interact, impacting use, non-use and sociotechnical reconfiguration.
Artificial intelligence (AI) can be appraised simultaneously as useful, capable, threatening, and demanding. Yet research typically treats attitudes toward AI as a single favorable-unfavorable continuum, obscuring how these evaluations coexist within individuals. Using a three-wave, time-lagged survey of 379 first-year university students in China, we examined configurations of six AI-related appraisal indicators: perceived humanlikeness, adaptability, and quality of AI; AI use anxiety; awareness of smart technology, artificial intelligence, robotics, and algorithms (STARA) as a career threat; and AI creative self-efficacy. Latent profile analysis supported four profiles: Positive Empowerment (19.2%), Anxious Acceptance (37.5%), Low-Perception Detached (37.2%), and High-Perception High-Vigilance (6.1%). R3STEP models showed that learning agility, digital literacy, future work self salience, and perceived university digital support were prospectively associated with profile membership, particularly in distinguishing the Low-Perception Detached profile from the more engaged profiles. BCH comparisons further showed that the Positive Empowerment and High-Perception High-Vigilance profiles reported relatively high levels of both career crafting and self-perceived employability, whereas the Low-Perception Detached profile reported the lowest levels of both outcomes. Notably, the High-Perception High-Vigilance profile combined elevated AI use anxiety and STARA awareness with strong career-development engagement, while the Low-Perception Detached profile combined comparatively low threat perceptions with weak career crafting and employability. These findings demonstrate that higher AI-related threat was not necessarily associated with weaker career preparation and, conversely, that low perceived threat was not necessarily associated with greater adaptive readiness. Overall, the results position students’ responses to AI as configurations of opportunity appraisal, threat, and AI creative self-efficacy rather than as uniformly positive or negative attitudes, thereby highlighting the need for differentiated university career education and AI-readiness interventions.
Examination of why and how users customize social AI through the lens of the newly developed concept of AI individualism shows that, for many users, customization is a co-creative process between the human and the AI that is perceived as strengthening support, autonomy, ownership, and engagement, potentially contributing to a closer and more personal relationship.
Marita Skjuve, Anna Grøndal Larsen, Asbjørn Følstad et al.· 0 citations
How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of millions of AI-related newspaper articles and social media posts grounded in 57 semi-structured interviews with AI professionals in 2021 and 2023--before and after the recent surge of public interest. We identify a range of sociological frames (interpretive schemas that structure collective cognition) and show how AI professionals use frames to address significant cognitive challenges, such as assigning responsibility for societal impacts. We develop a framework of three primary debates across which frames are adopted and contested: (i) the $\textit{method}$ of AI development, between frames of top-down expert systems and bottom-up emergent capabilities, (ii) the $\textit{mind}$ of an AI system, ranging from a passive tool to a humanlike"digital mind,"and (iii) the $\textit{morality}$ of how AI is used, particularly the decision of whether to slow down or speed up AI development. As humanity enters the era of transformative AI, technologists and policymakers must account for the framing dynamics that will circumscribe our beliefs, values, and actions.
Jacy Reese Anthis, Erik Brynjolfsson, James A. Evans· 0 citations
It is argued that psychological competence should become a core consideration for model providers, deploying organizations, researchers, and regulators concerned with the real-world effects of human-facing AI systems.
M. Economides, Paul M. Sacher, Samuel Salzer et al.· 0 citations