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Abstract
We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusing on environmental science applications. Our institute focuses on developing trustworthy AI methods for weather, climate, and coastal hazards. The AI methods will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and will be utilized by diverse, professional user groups to reduce risks to society. In addition, we are creating novel educational paths, including a new degree program at a community college serving underrepresented minorities, to improve workforce diversity for both AI and environmental science.
Abstract
We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusing on environmental science applications. Our institute focuses on developing trustworthy AI methods for weather, climate, and coastal hazards. The AI methods will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and will be utilized by diverse, professional user groups to reduce risks to society. In addition, we are creating novel educational paths, including a new degree program at a community college serving underrepresented minorities, to improve workforce diversity for both AI and environmental science.
Abstract
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. The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (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.
Abstract
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. The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (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.
