The FAIR Guiding Principles aim to improve findability, accessibility, interoperability and reusability for both humans and machines, initially aimed at scientific data, but also intended to apply to all sorts of research digital objects, with recent developments about their modification and application to software and computational workflows. In this position paper we argue that the FAIR principles also can apply to machine learning tools and models, though a direct application is not always possible as machine learning combines aspects of data and software. Here we discuss some of the elements of machine learning that lead to the need for some adaptation of the original FAIR principles, along with stakeholders that would benefit from this adaptation. We introduce the initial steps towards this adaptation, i.e., creating a community around it, some possible benefits beyond FAIR, and some of the open questions that such a community could tackle.