NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

McGovern, Amy; Bostrom, Ann; Davis, P. H.; Demuth, Julie L.; Ebert-Uphof, Imme; He, Ruoying; Hickey, Jason; Gagne, David John; Snook, Nathan; Stewart, Jebb Q.; Thorncroft, C. D.; Tissot, Philippe; Williams, John K.

doi:10.1175/bams-d-21-0020.1

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…There is no clear way to decide whether the model has been trained sufficiently, whether it performs well on a previously unseen set of inputs (generalizability) or, most importantly, whether we can meaningfully interpret its output (interpretability). Trustworthy ML is a growing area of interest as users favor models that are fair, reliable, and robust, which becomes especially important for high‐stakes decision making (e.g., McGovern et al., 2022). The precise definition of “trust” in a model depends on its application, but for integration into the model hierarchy, we aspire for a trustworthy ML model to be both generalizable and interpretable, as we do with any model.…”

Section: Data‐driven Methods: the Emergence Of Machine Learningmentioning

confidence: 99%

Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts

Mansfield,

Gupta,

Burnett

et al. 2023

J Adv Model Earth Syst

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The climate model hierarchy encompasses models of varying complexity along different axes, ranging from idealized models that elegantly describe isolated mechanisms to fully coupled Earth system models that aspire to provide useable climate projections. Based on the second Model Hierarchies Workshop, which took place in 2022, we present perspectives on how this field has evolved since the first Model Hierarchies Workshop in 2016. In this period, we have witnessed a dramatic increase in the use of (a) machine learning in climate modeling and (b) climate models to estimate risks and influence decision making under climate change. Here, we discuss the implications of these growing areas of research and how we expect them to become integrated into the model hierarchies framework.

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Section: Data‐driven Methods: the Emergence Of Machine Learningmentioning

confidence: 99%

Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts

Mansfield,

Gupta,

Burnett

et al. 2023

J Adv Model Earth Syst

View full text Add to dashboard Cite

show abstract

“…Trust in AI, and the extent to which AI is deemed trustworthy, is contingent on communications processes and products in AI, such as model or XAI outputs, or interfaces for imposing constraints on AI models; the visual presence of AI tends to increase trust in AI (Gilkson & Woolley, 2020). Many studies have called for or investigated explanations and XAI (McGovern, Bostrom, et al., 2022) as an approach to increasing trust (e.g., Hoffman et al., 2018; Lockey et al., 2021; Miller, 2019; Mueller et al., 2019; Tulio et al, 2007), while such explanations have often relied on visualizations (McGovern et al., 2019).…”

Section: Trust Risk and Scientific Uncertaintymentioning

confidence: 99%

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

Bostrom,

Demuth,

Wirz

et al. 2023

Risk Analysis

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Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human–AI teaming perspectives on AI development similarly underscore. Co‐development strategies may also help reconcile efforts to develop performance‐based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences.

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“…The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) is a convergent center focused on AI for the Earth and environmental sciences (ES) (McGovern et al. 2022). We are developing novel AI methods for real‐world high‐impact environmental use cases that ensure that we address the entire chain of relevant issues.…”

Section: Introduction To Ai2esmentioning

confidence: 99%

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

McGovern,

Ebert‐Uphoff,

Barnes

et al. 2024

AI Magazine

Self Cite

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The 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.

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NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

Cited by 7 publications

References 43 publications

Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts

Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts

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

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

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