AI & ML in Polar Science: Challenges and Best Practices

The webinar examined how Artificial Intelligence (AI) and Machine Learning (ML) are being applied to address the challenges of rapid environmental and social change in the Arctic and Antarctic. Presenters—including Johnathan Kool (Australian Antarctic Data Centre), Munish Madan (Arctic Institute of North America), and Christy Caudill (Canadian Biogenome Project)—shared insights on optimizing metadata, evaluating the capabilities and limitations of AI/ML in polar research, and co-developing AI tools with Indigenous Data Sovereignty considerations.

The session highlighted practical strategies for integrating FAIR principles into research workflows, discussed ethical frameworks for collaboration with Indigenous communities, and addressed challenges such as algorithmic bias and intellectual property concerns, using real-world case studies to illustrate best practices.