Trustworthy Artificial Intelligence for environmental sciences: An innovative approach for summer school

Many of our generation's most pressing environmental science problems are wicked problems, which means they cannot be cleanly isolated and solved with a single "correct" answer. (AI2ES) seeks to address such problems by developing synergistic approaches with a team of scientists from three disciplines: environmental science (including atmospheric, ocean, and other physical sciences), artificial intelligence (AI), and social science including risk communication. As part of our work, we developed a novel approach to summer school, held from 27 to 30 June 2022. The goal of this summer school was to teach a new generation of environmental scientists how to cross disciplines and develop approaches that integrate all three disciplinary perspectives and approaches in order to solve environmental science problems. In addition to a lecture series that focused on the synthesis of AI, environmental science, and risk communication, this year's summer school included a unique "trust-a-thon" component where participants gained hands-on experience applying both risk communication and explainable AI techniques to pretrained machine learning models. We had 677 participants from 63 countries register and attend online. Lecture topics included trust and trustworthiness (day 1), explainability and interpretability (day 2), data and workflows (day 3), and uncertainty quantification (day 4). For the trust-a-thon, we developed challenge problems for three different application domains: 1) severe storms, 2) tropical cyclones, and 3) space weather. Each domain had associated user persona to guide user-centered development.

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Related Links

Related Preprint #1 : Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI

Related Preprint #2 : The AI Index 2022 Annual Report

Related Software #1 : ai2es/tai4es-trustathon-2022: Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School 2022

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Author McGovern, A.
Gagne, David John
Wirz, Christopher D.
Ebert-Uphoff, I.
Bostrom, A.
Rao, Y.
Schumacher, A.
Flora, M.
Chase, R.
Mamalakis, A.
McGraw, M.
Lagerquist, R.
Redmon, R. J.
Peterson, Taysia
Publisher UCAR/NCAR - Library
Publication Date 2023-06-01T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Topic Category geoscientificInformation
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Metadata Date 2025-07-11T15:17:37.013888
Metadata Record Identifier edu.ucar.opensky::articles:26487
Metadata Language eng; USA
Suggested Citation McGovern, A., Gagne, David John, Wirz, Christopher D., Ebert-Uphoff, I., Bostrom, A., Rao, Y., Schumacher, A., Flora, M., Chase, R., Mamalakis, A., McGraw, M., Lagerquist, R., Redmon, R. J., Peterson, Taysia. (2023). Trustworthy Artificial Intelligence for environmental sciences: An innovative approach for summer school. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7183bjn. Accessed 03 August 2025.

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