Description
Opened by an inspirational keynote by Professor Julia Lane from New York University looking at Artificial Intelligence (AI) trends and how FAIR (Findable, Accessible, Interoperable, Reusable) principles are now being adapted in the context of AI models and datasets, the session continues with a panel aimed at further exploring the role of FAIR data for AI and AI for FAIR data. The panel discussion will address questions such as:
- Generative AI needs high-quality data: to what extent can FAIR metadata be useful for that?
- Can AI support researchers and data practitioners to automatically produce FAIR data and/or metadata?
- Can FAIR data improve the understanding of the provenance of AI models?
