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Cited by 51Human-Automation Interaction and SafetySocial Robot Interaction and HRIAccess Control and Trust

Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration

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McGrath, Melanie J., Duenser, Andreas, Lacey, Justine, Paris, Cécile·2025·Computers in Human Behavior: Artificial Humans
DOI 10.1016/j.chbah.2025.100200
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Abstract

Collaborative human-AI (HAI) teaming combines the unique skills and capabilities of humans and machines in sustained teaming interactions leveraging the strengths of each. In tasks involving regular exposure to novelty and uncertainty, collaboration between adaptive, creative humans and powerful, precise artificial intelligence (AI) promises new solutions and efficiencies. User trust is essential to creating and maintaining these collaborative relationships. Established models of trust in traditional forms of AI typically recognize the contribution of three primary categories of trust antecedents: characteristics of the human user, characteristics of the technology, and environmental factors. The emergence of HAI teams, however, requires an understanding of human trust that accounts for the specificity of task contexts and goals, integrates processes of interaction, and captures how trust evolves in a teaming environment over time. Drawing on both the psychological and computer science literature, the process framework of trust in collaborative HAI teams (CHAI-T) presented in this paper adopts the tripartite structure of antecedents established by earlier models, while incorporating team processes and performance phases to capture the dynamism inherent to trust in teaming contexts. These features enable active management of trust in collaborative AI systems, with practical implications for the design and deployment of collaborative HAI teams.

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  • Requiring user participation can appear as the CA shifting recovery responsibility onto the affected party, contradicting service recovery research where customers expect organizations to take responsibility for failures and make the necessary amends.

    “This can appear as the CA shifting recovery responsibility onto the affected party, contradicting service recovery research where customers expect organizations to take responsibility for failures and make the necessary amends (Choi et al., 2021;McGrath et al., 2025).”

    From Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies (2026) · cites this paper as McGrath et al., 2025

  • User-assisted repairs could also lead to a more tailored or accurate resolution if the user correctly assists the CA in resolving the error.

    “User-assisted repairs could also lead to a more tailored or accurate resolution if the user correctly assists the CA in resolving the error (Ashktorab et al., 2019;Collier et al., 2017;McGrath et al., 2025).”

    From Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies (2026) · cites this paper as McGrath et al., 2025

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Authors
McGrath, M. J., Duenser, A., Lacey, J., & Paris, C.
Affiliations
Commonwealth Scientific and Industrial Research Organisation (AU)
Corresponding author
McGrath, Melanie J.
ORCID
Venue
Computers in Human Behavior: Artificial Humans
Publisher
Elsevier BV
Published
2025-12
Volume
6
Pages
100200
ISSN
2949-8821 (ISSN-L)
DOI
10.1016/j.chbah.2025.100200
OpenAlex
W4413635962
Cited by
51
References
80
Citations by year
2026: 37 · 2025: 12 · 2024: 2
FWCI
54.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers in Human Behavior Artificial Humans (publishedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University) (cc-by)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Social Sciences › Sociology and Political Science › Access Control and Trust
Keywords
human-AI collaboration, trust in AI, antecedents of trust, trust management, team processes, performance phase, human-AI teaming

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