ArticleFrontiers in artificial intelligence2023
First impressions of a financial AI assistant: differences between high trust and low trust users.
Article in Frontiers in artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
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Who cites it
1 citing paper in PubMed.
- Survey on Chinese users' acceptance of AI assistants: expanding technology acceptance model.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Calibrating appropriate trust of non-expert users in artificial intelligence (AI) systems is a challenging yet crucial task. To align subjective levels of trust with the objective trustworthiness of a system, users need information about its strengths and weaknesses. The specific explanations that help individuals avoid over- or under-trust may vary depending on their initial perceptions of the system. In an online study, 127 participants watched a video of a financial AI assistant with varying degrees of decision agency. They generated 358 spontaneous text descriptions of the system and completed standard questionnaires from the Trust in Automation and Technology Acceptance literature (including perceived system competence, understandability, human-likeness, uncanniness, intention of developers, intention to use, and trust). Comparisons between a high trust and a low trust user group revealed significant differences in both open-ended and closed-ended answers. While high trust users characterized the AI assistant as more useful, competent, understandable, and humanlike, low trust users highlighted the system's uncanniness and potential dangers. Manipulating the AI assistant's agency had no influence on trust or intention to use. These findings are relevant for effective communication about AI and trust calibration of users who differ in their initial levels of trust.
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Registered trials
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.