Interpretability of AI models and FAIR for AI


The FAIR4HEP project is a DOE-funded collaboration of scientists at the UIUC, MIT, UCSD, UMN, and ANL whose purpose is to advance our understanding of the relationship between our data and artificial intelligence (AI) models by empowering scientists to explore both through the development of frameworks adhering to the principles of findability, accessibility, interoperability, and reusability (FAIR). Using high-energy physics (HEP) as the science use-case, this project will investigate FAIR ways to share our AI models and related data, create an environment where novel approaches to AI can be explored and applied to new data, and enable new insights for applying AI techniques.

IRIS-HEP topical meetings
Zoom Meeting ID
David Lange
Alternative hosts
Robert Currier Tuck, Shawn Mc Kee
Useful links
Join via phone
Zoom URL