AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography-Reference-Cited by-同舟云学术

AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography

Published:2024-02-14 Issue:1 Volume:45 Page:105-110
ISSN:0738-4602
Container-title:AI Magazine
language:en
Short-container-title:AI Magazine

Author:

McGovern Amy¹²^ORCID,Ebert‐Uphoff Imme³,Barnes Elizabeth A.⁴,Bostrom Ann⁵,Cains Mariana G.⁶,Davis Phillip⁷,Demuth Julie L.⁶,Diochnos Dimitrios I.²,Fagg Andrew H.²,Tissot Philippe⁸,Williams John K.⁹,Wirz Christopher D.⁶

Affiliation:

1. School of Meteorology University of Oklahoma Norman Oklahoma USA

2. School of Computer Science University of Oklahoma Norman Oklahoma USA

3. Electrical and Computer Engineering & Cooperative Institute for Research in the Atmosphere Colorado State University Fort Collins Colorado USA

4. Department of Atmospheric Science Colorado State University Fort Collins Colorado USA

5. Evans School of Public Policy and Governance University of Washington Seattle Washington USA

6. National Center for Atmospheric Research Boulder Colorado USA

7. Del Mar College Corpus Christi Texas USA

8. Conrad Blucher Institute Texas A&M University—Corpus Christi Corpus Christi Texas USA

9. The Weather Company IBM Business Armonk New York USA

Abstract

AbstractThe NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) focuses on creating trustworthy AI for a variety of environmental and Earth science phenomena. AI2ES includes leading experts from AI, atmospheric and ocean science, risk communication, and education, who work synergistically to develop and test trustworthy AI methods that transform our understanding and prediction of the environment. Trust is a social phenomenon, and our integration of risk communication research across AI2ES activities provides an empirical foundation for developing user‐informed, trustworthy AI. AI2ES also features activities to broaden participation and for workforce development that are fully integrated with AI2ES research on trustworthy AI, environmental science, and risk communication.

Funder

Directorate for Geosciences

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12160

Reference15 articles.

1. Sinh‐Arcsinh‐Normal Distributions to Add Uncertainty to Neural Network Regression Tasks: Applications to Tropical Cyclone Intensity Forecasts;Barnes E. A.;EDS,2023

2. This Looks Like That There: Interpretable Neural Networks for Image Tasks When Location Matters;Barnes E. A.;AIES,2022

3. Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences

4. Cains M. G. C. D.Wirz J. L.Demuth A.Bostrom A.McGovern I.Ebert‐Uphoff D. J.Gagne A.Burke andR.Sobash.2023. “Exploring what AI/ML Guidance Features NWS Forecasters Deem Trustworthy.” In103nd AMS Annual Meeting.AMS.

5. Diochnos D. I. andT. B.Trafalis.2021. “Learning Reliable Rules under Class Imbalance.” InSDM 28–36.