Regional and Mesoscale Meteorology Branch

Wirz, Christopher D., Julie L. Demuth, Ann Bostrom, Mariana G. Cains, Imme Ebert-Uphoff, David John Gagne II, Andrea Schumacher, Amy McGovern, and Deianna Madlambayan. “(Re) Conceptualizing trustworthy AI: A foundation for change.” Artificial Intelligence (2025): 104309.  https://doi.org/10.1016/j.artint.2025.104309.  

Summary:  The effort to make Artificial Intelligence (AI) methods “trustworthy” is important for many applications, including for meteorological applications. While developers and academics have grown increasingly interested in developing “trustworthy” artificial intelligence (AI), 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.  In this article we explore the various facets of what makes an AI system “trustworthy”.

Detailed Summary:  This article comes from the collaborative effort of several social scientists, meteorologists, and AI experts, to develop trustworthy AI algorithms for meteorological applications, as part of the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). 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). 
(POC: Imme Ebert-Uphoff, CIRA, iebert@colostate.edu.  Funding: NSF)