Who do we trust when we trust AI? New study reveals the surprising answer

Artificial intelligence has quietly slipped into nearly every corner of modern life, from predictive policing and automated welfare assessments to AI-assisted medical diagnostics and autonomous transport. Governments and corporations are deploying these systems to cut costs, boost efficiency and deliver new public services. Yet as AI becomes more pervasive, a deceptively simple question has become one of the most pressing in technology policy: why do ordinary citizens trust it? A new open-access study published in AI & Society by Steven David Pickering, Martin Ejnar Hansen and Yosuke Sunahara offers one of the most detailed answers yet, drawing on nationally representative surveys of more than 3,200 people in Japan and the United Kingdom, two democracies that have both embraced national AI strategies while struggling with chronically low trust in their own governments.

The researchers started from a theoretical framework known as trust transfer, the idea that confidence in a novel technology does not emerge in a vacuum but spills over from pre-existing trust in the people and institutions behind it. When citizens lack detailed knowledge of how a machine learning model actually works, they fall back on a cognitive shortcut: they judge the technology by the actors who develop, regulate and deploy it. Political science has long documented this logic. Trust, as Levi and Stoker famously argued, involves placing oneself in a position of vulnerability relative to an institution with the power to help or harm. The team therefore hypothesised that trust in government, trust in university scientists and generalised social trust, the belief that most people can be trusted, would each predict trust in AI independently of demographics and political ideology.

But institutional trust was only half the story. The study also tested psychological and affective mechanisms rooted in Bandura’s self-efficacy theory. People who feel competent to understand and use AI, the researchers reasoned, should trust it more, because perceived competence reduces apprehension and increases willingness to delegate decisions. Alongside self-efficacy, they measured technological optimism, the forward-looking belief that AI will produce meaningful societal benefits, and fear, captured by agreement with the blunt statement that AI scares me. Finally, they examined job displacement anxiety, one of the most personal dimensions of automation unease, asking employed respondents whether they believed AI would replace their job role entirely. Five hypotheses, two countries, one 1-to-7 trust scale: the design allowed the team to test whether the same psychological machinery operates in very different cultural and institutional environments.

The data came from parallel online surveys conducted in November 2024, using the YouGov panel in the United Kingdom with 1,040 respondents and the Rakuten Insight panel in Japan with 2,195 respondents. Even the descriptive statistics revealed striking national contrasts. Japanese respondents expressed markedly higher trust in AI on average, greater optimism about its benefits, measured across an eight-item scale with excellent internal consistency, and slightly higher self-efficacy. British respondents, by contrast, scored higher on generalised social trust and trust in university scientists, but reported significantly more fear of AI. Notably, both countries share an uncomfortable political baseline: according to OECD data, only about a quarter of respondents in each nation report high trust in their national government, making them ideal test cases for whether AI trust can flourish in low-trust environments.

The regression results delivered the study’s most robust finding: trust spills over, and it does so consistently in both countries. Higher trust in government, higher trust in university scientists and higher generalised social trust each predicted higher trust in AI, even after controlling for age, gender, education, ideology and household finances. The strength and direction of these effects were remarkably similar across the two national contexts, suggesting that AI is not evaluated as an isolated artefact but through a broader lens of institutional legitimacy. Citizens who believe that their political institutions, scientific community and fellow citizens are trustworthy extend that confidence to the abstract systems those actors build and oversee. For anyone designing AI governance, the implication is sobering: no amount of technical polish will compensate for a collapse in institutional credibility.

Self-efficacy also emerged as a significant positive predictor, confirming that people who feel they understand how AI can help them are more willing to trust it. But a sophisticated mediation analysis, following the causal-mechanism framework of Imai and colleagues, revealed a subtler picture. In Japan, self-efficacy retained a direct effect on trust even after accounting for optimism. In the UK, however, once technological optimism entered the models, the direct effect of self-efficacy became statistically indistinguishable from zero. In other words, British respondents’ confidence in their own abilities appears to build trust primarily by shaping their expectations about AI’s future benefits, while Japanese respondents seem to derive trust from competence itself. The indirect pathway through optimism accounted for a larger share of the total effect in the UK than in Japan, a genuine cross-national difference in the psychology of technological acceptance.

The affective findings were equally revealing. Optimism about AI’s societal role, from improving healthcare to reducing administrative burdens, increased trust in both countries, consistent with Slovic’s classic affect heuristic, in which positive emotions about perceived benefits systematically lower perceived risk. Fear worked in the opposite direction, but with a telling asymmetry: the negative effect of fear was stronger in the UK than in Japan. The authors suggest this may reflect a British public discourse that has framed AI more as a disruptive or alienating force, citing the House of Lords’ influential 2018 report era and subsequent risk-oriented media coverage. Emotion, in other words, is not noise on top of rational evaluation; it is a core channel through which technological trust is constructed, and its tone depends heavily on national conversation.

Then came the result nobody predicted. The job-risk hypothesis, grounded in prior evidence that workers who fear displacement trust AI less, held in neither country as theorised. In the UK, the relationship between believing AI will replace one’s job and trusting AI was statistically unstable and ultimately non-significant. In Japan, the association was not merely absent but reversed: respondents who believed AI would replace their job reported higher trust in AI, a pattern that survived every control and alternative specification. An interaction analysis added nuance. Among low risk-takers, job replacement perceptions barely mattered in either country. Among high risk-takers, UK respondents showed the expected negative pattern, while Japanese respondents remained positive even at high risk tolerance. The authors interpret this through Japan’s demographic reality: a society facing acute population ageing and labour shortages may frame automation not as a threat but as a pragmatic necessity, a tool for performing work that would otherwise go undone.

The study’s authors are careful about limits. The surveys measure attitudes, not behaviour, so the findings are associations rather than demonstrated causal effects, and the broad framing of artificial intelligence means the analysis cannot disentangle how people judge specific systems such as large language models versus industrial robots. Replication data and code are publicly available through the Harvard Dataverse, and the authors call for experimental designs to pin down the causal mechanisms. Still, the policy implications are concrete. Because institutional trust so reliably predicts AI trust, governance frameworks such as the EU AI Act that emphasise transparency, accountability and explainability matter not only for safety but for legitimacy. Because self-efficacy builds trust, public education campaigns, workplace training and civic participation in AI oversight could demystify the technology. And because fear weighs more heavily in some national conversations than others, risk communication must be tailored rather than one-size-fits-all.

Perhaps the deepest lesson is that trust in AI is not, at its foundation, about the machine at all. It is about who is seen to design, deploy and regulate it, whether citizens feel capable of engaging with it, and whether national narratives frame automation as renewal or rupture. The Japanese finding that job displacement beliefs can coexist with, and even feed, trust in AI shows that economic anxiety and technological optimism are not mutually exclusive; they can be woven together by context into a coherent public mood. As AI embeds itself further into health systems, welfare agencies and workplaces, the study suggests that democratic acceptance will depend less on blind confidence in algorithms and more on a well-informed sense of capability and control, resting on institutions that have earned the right to be trusted in the first place.

Subject of Research: Public trust in artificial intelligence and its institutional and psychological determinants in Japan and the United Kingdom

Article Title: Beyond the machine: risk, fear, optimism and the foundations of public trust in AI

Article References: Pickering, S. D., Hansen, M. E., & Sunahara, Y. (2026). Beyond the machine: risk, fear, optimism and the foundations of public trust in AI. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03312-2

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03312-2

Keywords: artificial intelligence, public trust, trust transfer, political trust, self-efficacy, technological optimism, job displacement, risk perception, AI governance, Japan, United Kingdom, survey research