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Presentation . 2022
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Presentation . 2022
License: CC BY
Data sources: Datacite
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Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School 2022

descriptionPublicationkeyboard_double_arrow_right Presentation , Other literature type 30 Jun 2022Publisher:ZenodoFunded by:NSF | AI Institute: Artificial ...
Authors: McGovern, Amy; Gagne, David; Ebert-Uphoff, Imme; Bostrom, Ann; Wirz, Christopher; Rao, Douglas; Schumacher, Andrea; +7 Authors

doi: 10.5281/zenodo.6784186 , 10.5281/zenodo.6784187

Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School 2022

Abstract

2022 online summer school on trustworthy AI for weather and climate applications. Participants will gain an understanding of: the foundations of trustworthiness for AI explanatory AI (XAI) and how explanations, physics, and robustness can help build trust in AI the relationship between ethics and trustworthiness how machine-learning systems have been developed for a range of environmental science applications

This material is based upon work supported by the National Science Foundation under Grant No. ICER-2019758.

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Keywords

risk communication, weather, trustworthy artificial intelligence, artificial intelligence, climate

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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