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

Bostrom, A., Demuth, J. L., Wirz, C. D., Cains, M. G., Schumacher, A., et al. (2024). Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences. Risk Analysis, doi:10.1111/risa.14245

Title Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences
Author(s) Ann Bostrom, Julie L. Demuth, Christopher D. Wirz, Mariana G. Cains, Andrea Schumacher, Deianna Madlambayan, Akansha Singh Bansal, Angela Bearth, Randy Chase, Katherine M. Crosman, Imme Ebert‐Uphoff, David John Gagne II, Seth Guikema, Robert Hoffman, Branden B. Johnson, Christina Kumler‐Bonfanti, John D. Lee, Anna Lowe, Amy McGovern, Vanessa Przybylo, Jacob T. Radford, Emilie Roth, Carly Sutter, Philippe Tissot, Paul Roebber, Jebb Q. Stewart, Miranda White, John K. Williams
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.
Publication Title Risk Analysis
Publication Date Jun 1, 2024
Publisher's Version of Record https://dx.doi.org/10.1111/risa.14245
OpenSky Citable URL https://n2t.net/ark:/85065/d7df6wf6
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