FAIR for Machine Learning; Building on the Lessons from FAIR Software

Ensuring that data are FAIR is nowadays a clear expectation across all science domains, as a result of many years of global efforts. Research software, has only just started to receive the same level of attention in recent years, with targeted actions towards the definition of the FAIR principles as applied to research software, as well as concerted efforts around reproducibility, quality, and sustainability. Given the rapid rise of ML as a key technology across all science domains, it is important to build on our collective experience, and already start addressing the challenges ahead of us, towards making ML FAIR.

This presentation was given in the context of the FAIR Data Symposium at the 23rd BioIT World Conference and Expo (15-16 April 2024).