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Abstract Artificial intelligence and machine learning (AI/ML) have attracted a great deal of attention from the atmospheric science community. The explosion of attention on AI/ML development carries implications for the operational community, prompting questions about how novel AI/ML advancements will translate from research into operations. However, the field lacks empirical evidence on how National Weather Service (NWS) forecasters, as key intended users, perceive AI/ML and its use in operational forecasting. This study addresses this crucial gap through structured interviews conducted with 29 NWS forecasters from October 2021 through July 2023 in which we explored their perceptions of AI/ML in forecasting. We found that forecasters generally prefer the term “machine learning” over “artificial intelligence” and that labeling a product as being AI/ML did not hurt perceptions of the products and made some forecasters more excited about the product. Forecasters also had a wide range of familiarity with AI/ML, and overall, they were (tentatively) open to the use of AI/ML in forecasting. We also provide examples of specific areas related to AI/ML that forecasters are excited or hopeful about and that they are concerned or worried about. One concern that was raised in several ways was that AI/ML could replace forecasters or remove them from the forecasting process. However, forecasters expressed a widespread and deep commitment to the best possible forecasts and services to uphold the agency mission using whatever tools or products that are available to assist them. Last, we note how forecasters’ perceptions evolved over the course of the study. 

This project developed a pre-interview survey, interview protocols, and materials for conducting interviews with expert users to better understand how they assess and make use decisions about new AI/ML guidance. Weather forecasters access and synthesize myriad sources of information when forecasting for high-impact, severe weather events. In recent years, artificial intelligence (AI) techniques have increasingly been used to produce new guidance tools with the goal of aiding weather forecasting, including for severe weather. For this study, we leveraged these advances to explore how National Weather Service (NWS) forecasters perceive the use of new AI guidance for forecasting severe hail and storm mode. We also specifically examine which guidance features are important for how forecasters assess the trustworthiness of new AI guidance. To this aim, we conducted online, structured interviews with NWS forecasters from across the Eastern, Central, and Southern Regions. The interviews covered the forecasters’ approaches and challenges for forecasting severe weather, perceptions of AI and its use in forecasting, and reactions to one of two experimental (i.e., non-operational) AI severe weather guidance: probability of severe hail or probability of storm mode. During the interview, the forecasters went through a self-guided review of different sets of information about the development (spin-up information, AI model technique, training of AI model, input information) and performance (verification metrics, interactive output, output comparison to operational guidance) of the presented guidance. The forecasters then assessed how the information influenced their perception of how trustworthy the guidance was and whether or not they would consider using it for forecasting. This project includes the pre-interview survey, survey data, interview protocols, and accompanying information boards used for the interviews. There is one set of interview materials in which AI/ML are mentioned throughout and another set where AI/ML were only mentioned at the end of the interviews. We did this to better understand how the label “AI/ML” did or did not affect how interviewees responded to interview questions and reviewed the information board. We also leverage think aloud methods with the information board, the instructions for which are included in the interview protocols. 

This project developed a pre-interview survey, interview protocols, and materials for conducting interviews with expert users to better understand how they assess and make use decisions about new AI/ML guidance. Weather forecasters access and synthesize myriad sources of information when forecasting for high-impact, severe weather events. In recent years, artificial intelligence (AI) techniques have increasingly been used to produce new guidance tools with the goal of aiding weather forecasting, including for severe weather. For this study, we leveraged these advances to explore how National Weather Service (NWS) forecasters perceive the use of new AI guidance for forecasting severe hail and storm mode. We also specifically examine which guidance features are important for how forecasters assess the trustworthiness of new AI guidance. To this aim, we conducted online, structured interviews with NWS forecasters from across the Eastern, Central, and Southern Regions. The interviews covered the forecasters’ approaches and challenges for forecasting severe weather, perceptions of AI and its use in forecasting, and reactions to one of two experimental (i.e., non-operational) AI severe weather guidance: probability of severe hail or probability of storm mode. During the interview, the forecasters went through a self-guided review of different sets of information about the development (spin-up information, AI model technique, training of AI model, input information) and performance (verification metrics, interactive output, output comparison to operational guidance) of the presented guidance. The forecasters then assessed how the information influenced their perception of how trustworthy the guidance was and whether or not they would consider using it for forecasting. This project includes the pre-interview survey, survey data, interview protocols, and accompanying information boards used for the interviews. There is one set of interview materials in which AI/ML are mentioned throughout and another set where AI/ML were only mentioned at the end of the interviews. We did this to better understand how the label “AI/ML” did or did not affect how interviewees responded to interview questions and reviewed the information board. We also leverage think aloud methods with the information board, the instructions for which are included in the interview protocols. 

Abstract Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision‐making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well‐documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision‐making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS. 

This project developed a pre-interview survey, interview protocols, and materials for conducting interviews with expert users to better understand how they assess and make use decisions about new AI/ML guidance. Weather forecasters access and synthesize myriad sources of information when forecasting for high-impact, severe weather events. In recent years, artificial intelligence (AI) techniques have increasingly been used to produce new guidance tools with the goal of aiding weather forecasting, including for severe weather. For this study, we leveraged these advances to explore how National Weather Service (NWS) forecasters perceive the use of new AI guidance for forecasting severe hail and storm mode. We also specifically examine which guidance features are important for how forecasters assess the trustworthiness of new AI guidance. To this aim, we conducted online, structured interviews with NWS forecasters from across the Eastern, Central, and Southern Regions. The interviews covered the forecasters’ approaches and challenges for forecasting severe weather, perceptions of AI and its use in forecasting, and reactions to one of two experimental (i.e., non-operational) AI severe weather guidance: probability of severe hail or probability of storm mode. During the interview, the forecasters went through a self-guided review of different sets of information about the development (spin-up information, AI model technique, training of AI model, input information) and performance (verification metrics, interactive output, output comparison to operational guidance) of the presented guidance. The forecasters then assessed how the information influenced their perception of how trustworthy the guidance was and whether or not they would consider using it for forecasting. This project includes the pre-interview survey, survey data, interview protocols, and accompanying information boards used for the interviews. There is one set of interview materials in which AI/ML are mentioned throughout and another set where AI/ML were only mentioned at the end of the interviews. We did this to better understand how the label “AI/ML” did or did not affect how interviewees responded to interview questions and reviewed the information board. We also leverage think aloud methods with the information board, the instructions for which are included in the interview protocols. 

Abstract 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. 

Abstract 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.