ArticleDiscover artificial intelligence2026
Identifying conceptual dimensions of trust in artificial intelligence from qualitative content analysis of open-ended responses.
Article in Discover artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
6 authors.
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Abstract
Background: Trust in artificial intelligence (AI) has received growing attention in academic research, ethical debates, and societal discourse. However, limited research has examined how laypeople conceptualize trust in AI assistants. Purpose: This study aims to identify recurring conceptual dimensions that underlie lay definitions of trust in AI. Methods: Open-ended survey responses were collected from English-speaking participants ( Results: Three core themes emerged. The most prevalent dimension mentioned was performance (85%), with participants defining trust primarily in terms of accuracy, source quality, and competence. The second dimension emphasized safety (38%), reflecting participants' concerns about harm prevention, protection and responsible system behavior. The third dimension involved perceived moral integrity of AI (26%), including references to unbiasedness, honesty, and fairness. Conclusions: The findings indicate that lay conceptualizations of AI trust are predominantly anchored in performance expectations, but with significant and noteworthy secondary emphasis on moral attributes related to in safety and integrity. These results offer an empirically grounded framework for understanding public trust in AI and may inform future measurement and design initiatives. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s44163-026-02150-x.
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