mlsanityData to 14 Oct 2026

Trust in AI and Its Role in the Acceptance of AI Technologies

arXiv 2203.12687 ↗First posted 23 Mar 2022cs.AI cs.HCcited by 29

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Abstract (arXiv)

As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of trust in the technology is required. This paper addresses this need by explaining the role of trust on the intention to use AI technologies. Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students. A path analysis confirmed that trust had a significant effect on the intention to use AI, which operated through perceived usefulness and participants' attitude toward voice assistants. In study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust. The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use. Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust. Overall, the role of trust in the acceptance of AI technologies was significant across both studies. This research contributes to the advancement and application of the TAM in AI-related applications and offers a multidimensional measure of trust that can be utilized in the future study of trustworthy AI.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 5 of its 75 references that are in the map. First posted on arXiv 23 Mar 2022.

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5 of 75 references.

References · 5

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  2. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  3. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  4. 2019Defining AI in Policy versus Practicecited by 15
  5. 2015Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to the iCub's answerscited by 2

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Citing papers per year: 2023: 7; 2024: 7; 2025: 11; 2026: 4.

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  1. 2023MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Controlcited by 11
  2. 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 11
  3. 2023Anthropomorphization of AI: Opportunities and Riskscited by 8
  4. 2023Learning to Prompt in the Classroom to Understand AI Limits: A pilot studycited by 6
  5. 2023Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender Systemcited by 5
  6. 2024Explainability Paths for Sustained Artistic Practice with AIcited by 4
  7. 2024Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategycited by 4
  8. 2025Understanding Human-AI Trust in Educationcited by 4
  9. 2025LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Modelscited by 3
  10. 2023A multilevel framework for AI governancecited by 2
  11. 2023Can I say, now machines can think?cited by 2
  12. 2024Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMscited by 2
17 more
  1. 2024Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generatedcited by 2
  2. 2025A closer look at how large language models trust humans: patterns and biasescited by 2
  3. 2025Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impairedcited by 2
  4. 2025Revisiting Trust in the Era of Generative AI: Factorial Structure and Latent Profilescited by 2
  5. 2023Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Workcited by 1
  6. 2025From Explainability to Action: A Generative Operational Framework for Integrating XAI in Clinical Mental Health Screeningcited by 1
  7. 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systemscited by 1
  8. 2024The impact of labeling automotive AI as "trustworthy" or "reliable" on user evaluation and technology acceptancecited by 0
  9. 2024Perceptions of Discriminatory Decisions of Artificial Intelligence: Unpacking the Role of Individual Characteristicscited by 0
  10. 2025The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experimentcited by 0
  11. 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectivescited by 0
  12. 2025Evaluation and Incident Prevention in an Enterprise AI Assistantcited by 0
  13. 2025Emotionally Vulnerable Subtype of Internet Gaming Disorder: Measuring and Exploring the Pathology of Problematic Generative AI Usecited by 0
  14. 2025Simulating Teams with LLM Agents: Interactive 2D Environments for Studying Human-AI Dynamicscited by 0
  15. 2026Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interactioncited by 0
  16. 2026Beyond Adoption Intention How Trust in Augmented Analytics Relates to Perceived Decision Quality Among Non-Technical BI Userscited by 0
  17. 2026AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systemscited by 0