Integrating artificial intelligence with expert knowledge in global environmental assessments: opportunities, challenges and the way ahead

Journal article

With new cycles of global environmental assessments (GEAs) recently starting, including GEO-7 and IPCC AR7, there is increasing need for artificial intelligence (AI) to support in synthesising the rapidly growing body of evidence for authors and users of these assessments. In this article, we explore recent advances in AI and connect them to the different stages of GEAs showing how some processes can be automatised and streamlined. The meticulous and labour-intensive nature of GEAs serves as both a valuable strength and a challenge to staying pertinent and current in today’s era of urgency and the pursuit of the latest knowledge. Utilising AI tools for reviewing and synthesizing scientific literature holds the evident promise of substantially lessening the workload for experts and expediting the assessment process. This, in turn, could lead to more frequent report releases and a smoother integration of the latest scientific advancements into actionable measures. However, successful outcomes can only be achieved if domain experts co-develop and oversee the deployment of such tools together with AI researchers. Otherwise, these tools run the risk of producing inaccurate, incomplete, or misleading information with significant consequences. We demonstrate this through a few examples that compare recently deployed large language models (LLMs) based tools in their performance in capturing nuanced concepts in the context of the reports of the Intergovernmental Panel on Climate Change (IPCC). We recommend establishing ethical committees and organising dedicated expert meetings to develop best practice guidelines, ensuring responsible and transparent integration of AI into GEAs.

Authors: Muccione, V; Vaghefi, SA; Bingler, J; Allen, SK; Kraus, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Regional Environmental Change
• Volume: 24
• Issue: 3
• Article number: 121
• Publication date: 2024-08-02
• Acceptance date: 2024-07-21
• DOI: 10.1007/s10113-024-02283-8
• EISSN: 1436-378X
• ISSN: 1436-3798
• Language: English
• Keywords: Artificial intelligence; Climate change; Large language models; Global environmental assessments