Qualitative survey maps three dimensions of lay trust in AI

A qualitative analysis of open-ended survey responses found that people define trust in AI assistants through three recurring dimensions: performance, safety and perceived moral integrity. Performance was mentioned in 85% of responses to the AI-trust question, compared with 38% for safety and 26% for integrity. The pattern indicates that users generally begin with whether an assistant produces accurate, reliable and useful output, but many also expect it to protect personal information, prevent harm, act without hidden agendas and provide unbiased answers. Trust in generative AI therefore appeared in the responses as a multidimensional judgment combining technical reliability with expectations that are often associated with human relationships.

The study recruited English-speaking participants through Prolific and the undergraduate participant pool of a large North American university. The initial sample comprised 204 people: 100 Prolific participants and 104 students. After responses considered omitted or nonsensical were removed, the analytic sample included 169 answers to the AI-assistant question and 175 answers to a question about trust in general. The AI responses ranged from 1 to 101 words, with a mean of 15.32 words, while general-trust responses ranged from 1 to 85 words and averaged 12.65 words. Researchers used inductive qualitative content analysis within a postpositivist framework, breaking responses into meaningful semantic units and allowing categories to develop from participants’ wording rather than imposing a predefined model of trust.

Performance was by far the dominant dimension. Participants described an AI assistant as trustworthy when it was accurate, dependable, competent, consistent and able to complete requested tasks effectively. Among the performance-related references, 57% concerned accuracy, 17% concerned reliability, 7.6% referred to source quality and 5.5% concerned competence. Smaller groups mentioned helpfulness, behaving as intended, transparent reasoning, consistency and the ability to depend on the system. Participants did not want an assistant merely to produce a plausible answer: they expected information to be correct, factually reliable and connected to reputable, up-to-date sources. Some also wanted the assistant not to fabricate information simply to respond. This prominence distinguishes the spontaneous definitions obtained in the study from earlier multidimensional measures that grouped capability and reliability together without explicitly treating accuracy as a separate concern.

Safety was the second major theme, appearing in about 38% of AI-trust responses and most often in connection with privacy and confidentiality. Participants said they expected shared information to remain confidential, personal disclosures to be protected and the assistant to prioritize the user’s interests rather than pursue concealed agendas. Some described trust as feeling secure about what they type into the system, while others said they would accept an answer without continually checking it. The latter view reflects a stated willingness to suspend doubt and resembles the human-factors concept of automation bias, in which people accept automated outputs uncritically. The safety category therefore captured both a request for protective safeguards and, in some cases, a readiness to rely on the assistant without further verification.

Perceived moral integrity accounted for 26% of AI-trust responses. Participants associated trustworthy AI with being honest, unbiased, fair and impartial, as well as free from hidden motives. They also sometimes directed these expectations toward the organization or regulatory framework behind an assistant rather than describing the model itself as a moral agent. At the same time, several participants explicitly noted that AI does not possess emotions or motives like a human and therefore considered reliability, consistency and the integrity of its output more relevant. The analysis distinguished honesty from transparency: honesty meant that an assistant told the truth and was not perceived as deceptive, while transparency meant that it disclosed information about its operation, sources or data use. A system could therefore be regarded as transparent without being trusted to tell the truth, or as honest without being transparent.

Comparing AI trust with participants’ broader conception of trust showed a shift in emphasis rather than an entirely different set of ideas. Performance remained the leading category in general trust, but it appeared in 51% of responses, well below the 85% recorded for AI. Integrity and character were mentioned in 36% of general-trust responses, safety in 31%, and a separate category that the researchers labeled faith in 12.5%. Within the general concept of trust, respondents more often emphasized reliability, dependability, honesty and loyalty. Accuracy was comparatively uncommon, whereas faith described accepting an answer without proof or investigation. Loyalty and fears of being betrayed were prominent in descriptions of general trust, but the reported AI responses did not establish betrayal as a defining expectation for AI assistants. The comparison suggests that people retain broad interpersonal ideas about trust but place greater weight on functional quality when the object of trust is an AI assistant.

The results are compatible with two complementary perspectives used in human–AI research. A systems perspective treats trust as confidence in mechanisms, standards, accountability structures and technological infrastructure, much as a rideshare passenger may rely on ratings, screening and complaint procedures rather than personal knowledge of a driver. An interpersonal perspective emphasizes competence, benevolence and integrity and helps explain why some participants assigned quasi-moral qualities to AI. Performance comments often appeared to concern the broader system’s capacity to generate dependable outputs, while expectations about privacy, source disclosure and data use could point toward the company or infrastructure supporting the assistant. At the same time, demands that an AI never fabricate information, prioritize the user’s goals or act without hidden agendas can sound like moral accountability directed at an autonomous actor. The open-ended responses do not establish that AI trust is fundamentally interpersonal or technical; they show that both forms of reasoning were present in the same population.

Participants also linked trust to transparency, helpfulness and appropriate behavior. Requests for understandable reasoning and clear disclosure of sources were consistent with arguments that transparency can support trust in AI, although the survey cannot determine whether transparency caused confidence or merely appeared alongside it. Helpfulness mattered in practical terms: some participants said they would extend trust after an assistant had demonstrated useful and genuinely supportive assistance. Competence was expressed as completing a task efficiently, while “behaving as intended” meant doing what was requested and nothing else. The design of the study is important because it began with participants’ spontaneous descriptions rather than asking them to rate predefined trust dimensions. That approach exposed accuracy as a particularly prominent concern and revealed the coexistence of technical, protective and moral expectations. It did not test whether any one attribute changes behavior, reliance or willingness to use an assistant.

Several limitations constrain the conclusions. The cross-sectional design cannot establish that accuracy, safety, integrity or any other attribute causes trust to form, and it cannot show how attitudes change over time. Brief open-ended answers offered broad coverage but less interpretive depth than interviews. Recruitment through online platforms and a university participant pool used convenience sampling: the Prolific group included 56 people from the United States, 26 from the United Kingdom, 17 from Canada and one from France, while the remaining participants were undergraduates. Online recruitment may also have favored people who were digitally engaged and willing to take part in AI research, potentially underrepresenting other attitudes. In addition, participants saw a standardized definition of an AI assistant and answered other questions about generative AI before the open-ended items, so contextual priming cannot be excluded. Methodological checks nevertheless showed substantial coder agreement: Cohen’s kappa ranged from 0.68 to 0.78 across major codes, with percent agreement from 86% to 95%.

The study’s main contribution is an empirically grounded map of the expectations that these English-speaking participants brought to a relatively new trust question. Their descriptions place accurate, dependable and useful performance at the center, but do not stop there. They also ask for privacy, protection from harm, freedom from hidden agendas, resistance to bias, honesty and accountable disclosure of sources or data use. Because many responses crossed more than one category, lay trust in AI cannot be reduced to whether an assistant sounds truthful or performs a task successfully. The findings provide a starting framework for future measurement and design work, while longitudinal studies, experimental manipulations and in-depth interviews would be needed to test how the identified dimensions relate to trust formation, reliance and behavior over time and across different populations and applications.

Subject of Research: Laypeople’s conceptual dimensions of trust in AI assistants

Article Title: Identifying conceptual dimensions of trust in artificial intelligence from qualitative content analysis of open-ended responses

Article References: Toudjui, S., Tang, J., Akhter, H., Cho, E., Côté, É., & Plaks, J. E. (2026). Identifying conceptual dimensions of trust in artificial intelligence from qualitative content analysis of open-ended responses. Discover Artificial Intelligence, 6(1), Article 1036. https://doi.org/10.1007/s44163-026-02150-x

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02150-x

Keywords: artificial intelligence, trust in AI, generative AI, qualitative analysis, open-ended survey, AI performance, AI safety, privacy, moral integrity, accuracy, Identifying, conceptual