(Re)Conceptualizing trustworthy AI: A foundation for change

Developers and academics have grown increasingly interested in developing “trustworthy” artificial intelligence (AI). However, this aim is difficult to achieve in practice, especially given trust and trustworthiness are complex, multifaceted concepts that cannot be completely guaranteed nor built entirely into an AI system. We have drawn on the breadth of trust-related literature across multiple disciplines and fields to synthesize knowledge pertaining to interpersonal trust, trust in automation, and risk and trust. Based on this review we have (re)conceptualized trustworthiness in practice as being both (a) perceptual, meaning that a user assesses whether, when, and to what extent AI model output is trustworthy, even if it has been developed in adherence to AI trustworthiness standards, and (b) context-dependent, meaning that a user's perceived trustworthiness and use of an AI model can vary based on the specifics of their situation (e.g., time-pressures for decision-making, high-stakes decisions). We provide our reconceptualization to nuance how trustworthiness is thought about, studied, and evaluated by the AI community in ways that are more aligned with past theoretical research.

To Access Resource:

Questions? Email Resource Support Contact:

  • opensky@ucar.edu
    UCAR/NCAR - Library

Resource Type publication
Temporal Range Begin N/A
Temporal Range End N/A
Temporal Resolution N/A
Bounding Box North Lat N/A
Bounding Box South Lat N/A
Bounding Box West Long N/A
Bounding Box East Long N/A
Spatial Representation N/A
Spatial Resolution N/A
Related Links N/A
Additional Information N/A
Resource Format PDF
Standardized Resource Format PDF
Asset Size N/A
Legal Constraints

Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Access Constraints None
Software Implementation Language N/A

Resource Support Name N/A
Resource Support Email opensky@ucar.edu
Resource Support Organization UCAR/NCAR - Library
Distributor N/A
Metadata Contact Name N/A
Metadata Contact Email opensky@ucar.edu
Metadata Contact Organization UCAR/NCAR - Library

Author Wirz, Christopher D.
Demuth, Julie L.
Bostrom, A.
Cains, Mariana
Ebert-Uphoff, I.
Gagne, David John
Schumacher, Andrea
McGovern, A.
Madlambayan, D.
Publisher UCAR/NCAR - Library
Publication Date 2025-05-01T00:00:00
Digital Object Identifier (DOI) Not Assigned
Alternate Identifier N/A
Resource Version N/A
Topic Category geoscientificInformation
Progress N/A
Metadata Date 2025-07-10T19:47:02.464056
Metadata Record Identifier edu.ucar.opensky::articles:43150
Metadata Language eng; USA
Suggested Citation Wirz, Christopher D., Demuth, Julie L., Bostrom, A., Cains, Mariana, Ebert-Uphoff, I., Gagne, David John, Schumacher, Andrea, McGovern, A., Madlambayan, D.. (2025). (Re)Conceptualizing trustworthy AI: A foundation for change. UCAR/NCAR - Library. https://n2t.net/ark:/85065/d7m3315j. Accessed 02 August 2025.

Harvest Source