mlsanityData to 7 Oct 2026

Challenges and Trends in User Trust Discourse in AI

arXiv 2305.11876 ↗First posted 5 May 2023cs.HC cs.AI cs.CYcited by 1

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Abstract (arXiv)

The Internet revolution in 1990, followed by the data-driven and information revolution, has transformed the world as we know it. Nowadays, what seam to be 10 to 20 years ago, a science fiction idea (i.e., machines dominating the world) is seen as possible. This revolution also brought a need for new regulatory practices where user trust and artificial Intelligence (AI) discourse has a central role. This work aims to clarify some misconceptions about user trust in AI discourse and fight the tendency to design vulnerable interactions that lead to further breaches of trust, both real and perceived. Findings illustrate the lack of clarity in understanding user trust and its effects on computer science, especially in measuring user trust characteristics. It argues for clarifying those notions to avoid possible trust gaps and misinterpretations in AI adoption and appropriation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 99 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

7 of 99 references.

References · 7

  1. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  2. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
  3. 2020The Pragmatic Turn in Explainable Artificial Intelligence (XAI)cited by 53
  4. 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
  5. 2020Measure Utility, Gain Trust: Practical Advice for XAI Researchercited by 9
  6. 2019Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Developmentcited by 8
  7. 2020Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactionscited by 4

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