The idea of FAIR in the context of scientific data management and stewardship was developed in 2014 and turned into specific principles in 2016. Along the way, the idea was generalized in concept to apply to both data and other digital scholarly objects, but it has become clear in practice that what works for data does not directly work for all other digital objects. Both previous and ongoing work show that many of the guiding FAIR principles need to either be re-written or reinterpretted for software, and this is being done. This poster discusses the beginning of a process for extending of the FAIR principles to machine learning (ML) models, which have characteristics of both data and software.

The poster is available on Zenodo .