TM2 | Ethical AI/ML in the Earth, Space, and Environmental Sciences: Risks, Rewards, Principles, and Responsibilities for Researchers
Ethical AI/ML in the Earth, Space, and Environmental Sciences: Risks, Rewards, Principles, and Responsibilities for Researchers
Convener: Shelley Stall | Co-convener: Kristina VrouwenvelderECSECS
Thu, 18 Apr, 19:00–20:00 (CEST)
 
Room L2
Thu, 19:00
"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 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 already 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, we will discuss a recent multi-disciplinary community report facilitated by AGU and funded by the U.S. National Aeronautics and Space Administration developing a set of principles and responsibilities for using AI and ML in Earth, space, and environmental science-focused research. In this town hall, we will feature short presentations from leaders in the community, who will highlight the report outcomes and outline opportunities, risks, and needs for disciplines across the Earth, space, and environmental sciences. Afterwards, we will host a panel discussion with all the speakers to start a community conversation about related needs across these areas. Finally, we will ask for feedback on a plan to convene the community mid-2024 to make any needed updates to the community report as the research landscape continues to rapidly evolve as AI and ML tools advance.

In this town hall we invite the EGU community to participate in preparing the next version of the report scheduled for 2024. Work in this area is moving quickly and we see a strong need to include more robust language around the use of LLMs, quality of datasets used to train AI models, and importance of FAIR data, and would like to incorporate your ideas too.

Topics include:
AI and ML use across EGU divisions and disciplines
Analysis-ready data to enable AI and ML methods
Ethical considerations and practices for AI and ML in research"