Submitted by Fotis Psomopoulos
The main focus of the meeting is to push forward the discussion on the FAIR principles, and the way they could be applied in a Machine Learning context. Specifically, the discussion will be focussed under the following two main question lines:
How do you think FAIR should be applied to ML? What changes/definitions are most needed? What questions need most discussion?
Should this address only ML models, and/or also processes, and/or also platforms, etc.?
In order to facilitate the discussion, people from relevant activities, initiatives and projects will be invited to offer their perspective. Such initiatives include the Pistoia Alliance, ELIXIR, the CLAIRE network and FAIR4HEP among others. Given the online form of the Plenary, some of these perspectives may be delivered as pre-recorded videos.
Ultimately, the main outcome of this session would be to build upon the effort so far and identify the concrete next steps forward.
Collaborative session notes: https://docs.google.com/document/d/1gs5AYPcQFk4YRbwRf8fyFmqmK2x9Em5jCYA-7gjW6PI/edit?usp=sharing
Introduction(s) (10 min):
Talks (5 + 2 min each), focus on FAIR for ML in different contexts. Tentative list:
Discussion & next steps (30 min)
Should FAIR address only ML models, and/or also processes, and/or also platforms?
How do you think FAIR should be applied to ML? Reuse FAIR for data and software, or aim towards a redefinition?
What changes/definitions are most needed? What questions need most discussion?
Should we propose a formal structure under RDA (IG, WG, CoP)?
Short introduction describing any previous activities:
There is a large amount of FAIR work, both in RDA and elsewhere, initially focused on data and now software and other products but generally not ML models. Some of the speakers in this session are involved in projects where FAIR for ML models is a topic of discussion. Additionally, we presented poster 31b (FAIR principles for ML models - https://doi.org/10.5281/zenodo.4271995) at RDA VP16 to start discussion on this at RDA, with a dedicated BoF session at RDA VP17 (https://www.rd-alliance.org/defining-fair-machine-learning-ml) that aimed to capture the overall perspective on the topic.
The discussion around FAIR for Machine Learning continued in further events under different domains; during the FAIR Festival, the efforts of FAIR4ML were presented together with similar initiatives for Software and Workflows. During ESIP 2021 in July 2021, the initiative was presented in the context of “Best Practices for Reusability of Machine Learning Models: Guideline and Specification”, with a first informal Community Call taking place in the same month as well. Having a critical mass of interested parties captured already, the main purpose of the VP18 BoF session will be to identify concrete next steps forward.
BoF applicant serving as contact person:
Additional links to informative material:
Daniel S. Katz (University of Illinois at Urbana-Champaign), Fotis E. Psomopoulos (Institute of Applied Biosciences, Centre for Research and Technology Hellas), additional speakers tbc
Avoid conflict with the following group (1):
Avoid conflict with the following group (2):