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Article published In: Multidisciplinary Perspectives on Human-AI Team Trust
Edited by Nicolo' Brandizzi, Morgan Elizabeth Bailey, Carolina Centeio Jorge, Myke C. Cohen, Francesco Frattolillo and Alan Richard Wagner
[Interaction Studies 26:2] 2025
► pp. 164–199

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Multidisciplinary perspectives on human‑AI team trust

ORCiD logo with link | Delft University of TechnologyDelft
ORCiD logo with link | University of GlasgowGlasgow
ORCiD logo with link | Sapienza University of RomeRome
ORCiD logo with link | Delft University of TechnologyDelft
ORCiD logo with link | Eindhoven University of TechnologyEindhoven
ORCiD logo with link | DFKISaarbrücken
Published online: 27 February 2026
DOI logo with link
https://doi.org/10.1075/is.24048.tie
Abstract
Human-AI teamwork is no longer a topic of the future. Given the importance of trust in human teams, the question arises how trust functions in human-AI teams. Although trust has long been studied from a human-centred perspective (e.g. in psychology and philosophy), a computational perspective and from the perspective of human trust in AI (e.g. in human-computer interaction), the study of trust in human-AI interaction in a team setting is still a novel field. For this reason, the MULTITTRUST (Multidisciplinary perspectives on Human-AI Team Trust) workshop series was founded. In this paper, we present the main outcomes after three editions. Our contributions are: an overview of the shared language of concepts and definitions; an outline of the main open research challenges; and methodological guidelines for further studies in meaningful human-AI team trust. These three contributions form a foundational roadmap towards a better understanding of trust in human-AI team interactions.
Article outline
  • 1.Introduction
  • 2.Concepts and definitions
    • 2.1Trust
      • 2.1.1Trust across disciplines
    • 2.2Trustworthiness
    • 2.3Trust calibration
    • 2.4Artificial trust
    • 2.5Team trust
  • 3.Research challenges
    • 3.1Trust calibration
      • Challenge. How can AI understand a human’s trust?
      • Challenge. How can AI influence a human’s trust?
      • Challenge. How do we deal with the subjective and dynamic nature of trust when trying to observe it?
      • Challenge. Can we separate trust in an agent from trust in the humans behind it?
      • Challenge. Can we quantify how much trust is appropriate in a given situation?
      • Challenge. How can we compare human trust to an agent’s trustworthiness in a meaningful way?
      • Challenge. What strategies should AI systems use to increase or dampen trust?
      • Challenge. Are there trade-offs to more calibrated trust, which might not be worth it?
      • Challenge. How can trust calibration take into account changing contexts as AI itself evolves?
    • 3.2Artificial trust
      • Challenge. What can we observe to base artificial trust on?
      • Challenge. How do we deal with the lack of ground truth?
      • Challenge. How do we determine the right goal of artificial trust?
      • Challenge. How do we make an agent transparent about artificial trust?
    • 3.3Team trust
      • Challenge. How do human trust and artificial trust differ, and how can they be compared and integrated in team trust?
      • Challenge. Do beliefs exist that are shared between humans and AI?
      • Challenge. Should we strive to model human-AI team trust after team trust in humans, or is it something completely new?
  • 4.Measures and methods
    • 4.1Trust measures
    • 4.2Research setting
  • 5.Conclusions
  • Author’s contributions
  • Acknowledgements
  • Notes
  • References
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