AGU Learning Session: Garbage In, Garbage Out: Mitigating the Ethical Risks of AI and ML
Year: 2024
Type: article
Abstract: AGU Learning Session delivered on 12 Dec 2024. Abstract Artificial intelligence (AI) and machine learning (ML) are enabling advances in understanding the Earth and its systems at all scales and are increasingly being used in diverse health and societal applications to address urgent issues including climate change and natural hazards. Moreover, the recent release of powerful large-language models including Chat GPT is creating radical change in scholarly publishing, education, and beyond. To address the urgent need for guidance on the ethical use of AI and ML in earth, space, and environmental science-focused research, a recent multi-disciplinary community report facilitated by American Geophysical Union and funded by the U.S. National Aeronautics and Space Administration introduces a set of principles and responsibilities for using AI and ML in Earth, space, and environmental science-focused research. These principles are largely adoptable by most disciplines and important to incorporate into the research ecosystem and fundamental expectations such as an ethical Code of Conduct for research. In this talk, the process for developing the Ethical framework for AI and ML will be shared along with an overview of the principles for organizations and researchers. Funder: NASA (80NSSC22K0734) Project Info: https://data.agu.org/ethics-ai-ml/ Report: Stall, et al. Ethical and Responsible Use of AI/ML in the Earth, Space, and Environmental Sciences . ESS Open Archive . April 12, 2023. DOI: 10.22541/essoar.168132856.66485758/v1 Nature Commentary connecting the AI/ML Ethics to FAIR Data to increase quality of training data improving results: Hanson, B., Stall, S., Cutcher-Gershenfeld, J., Vrouwenvelder, K., Wirz, C., Rao, Y., & Peng, G. (2023). Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research. In Nature (Vol. 623, Issue 7985, pp. 28–31). Springer Science and Business Media LLC. https://doi.org/10.1038/d41586-023-03316-8 Dr. Manil Maskey, AI Lead, NASA, BIFOLD Berlin Keynote Talk: https://youtu.be/vW_Q6SofWBA?si=5YitrgVA5pwm7iid These slides were originally presented at EuroTOX 2024.
Author: Stall, Shelley
Institution: American Geophysical Union
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6930633813
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