mlsanityData to 7 Oct 2026

Human-centered trust framework: An HCI perspective

arXiv 2305.03306 ↗First posted 5 May 2023cs.HC cs.AI cs.CYcited by 2

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Abstract (arXiv)

The rationale of this work is based on the current user trust discourse of Artificial Intelligence (AI). We aim to produce novel HCI approaches that use trust as a facilitator for the uptake (or appropriation) of current technologies. We propose a framework (HCTFrame) to guide non-experts to unlock the full potential of user trust in AI design. Results derived from a data triangulation of findings from three literature reviews demystify some misconceptions of user trust in computer science and AI discourse, and three case studies are conducted to assess the effectiveness of a psychometric scale in mapping potential users' trust breakdowns and concerns. This work primarily contributes to the fight against the tendency to design technical-centered vulnerable interactions, which can eventually lead to additional real and perceived breaches of trust. The proposed framework can be used to guide system designers on how to map and define user trust and the socioethical and organisational needs and characteristics of AI system design. It can also guide AI system designers on how to develop a prototype and operationalise a solution that meets user trust requirements. The article ends by providing some user research tools that can be employed to measure users' trust intentions and behaviours towards a proposed solution.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 70 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

7 of 70 references.

References · 7

  1. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
  2. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  3. 2020Trustworthy AIcited by 62
  4. 2020The Pragmatic Turn in Explainable Artificial Intelligence (XAI)cited by 53
  5. 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
  6. 2020Measure Utility, Gain Trust: Practical Advice for XAI Researchercited by 9
  7. 2019Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Developmentcited by 8

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.