Evidence map›Paper›PMID 41595036›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

Beyond Risk Reduction: Vigilant Trust in Artificial Intelligence Based on Evidence from China.

Wuyao Ding, Yun Wu, Junxiu Wang

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Wuyao DingSchool of Mental Health, Wenzhou Medical University, Wenzhou 325035, China.ORCID 0000-0003-0592-5371
Yun WuChinese Academy of Social Sciences, Beijing 100732, China.ORCID 0000-0002-8298-4909
Junxiu WangSchool of Mental Health, Wenzhou Medical University, Wenzhou 325035, China.ORCID 0000-0001-8972-3923

Funding

Chinese Postdoctoral Science Foundation 2025M773459Zhejiang Provincial Philosophy and Social Sciences Planning Project 25YJRC008ZDZhejiang Provincial Philosophy and Social Sciences Planning Project 25YJRC008ZD-3
6 · The paper itself

Abstract

Public trust in artificial intelligence (AI) is often assumed to promote acceptance by reducing perceived risks. Using a nationally representative survey of 10,294 Chinese adults, this study challenges that assumption and introduces the concept of vigilant trust. We argue that trust in AI does not necessarily diminish risk awareness but can coexist with, and even intensify, attention to potential harms. By examining four dimensions of trust-trusting stance, competence, benevolence, and integrity-we find that all of them consistently enhance perceived benefits, which emerge as the strongest predictor of AI acceptance. However, trust shows differentiated relationships with perceived risks: benevolence reduces risk perception, whereas trusting stance is associated with higher perceptions of both benefits and risks. Perceived risks do not uniformly deter acceptance and, in some contexts, are positively associated with willingness to adopt AI. By moving beyond the conventional view of trust as a risk-reduction mechanism, this study conceptualizes vigilant trust as a mode of engagement in which openness to AI is accompanied by sustained awareness of uncertainty. The findings offer a more nuanced understanding of public acceptance of AI and its implications for governance and communication.

Indexed as

AI acceptanceperceived benefitsperceived riskstrustvigilance

Identifiers

PMID41595036
PMCPMC12837474

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.