[Fotis Psomopoulos]

The meeting will comprise 2 parts;
The first part of the session will be dedicated to presenting the current structure and planned efforts of the FAIR4ML IG, as well as having updates on the two Task Forces that were initiated since the last plenary. It will also include some targeted talks from relevant projects or activities, to set the overall stage.
The second half of the session will be dedicated to discussing the work on the Task Forces, as well as review potential new ones.
 
Specifically, a tentative agenda is the following:

Introduction(s) (5 min): 

Intro to FAIR 4 ML IG, the objectives and structure

Part 1: 

Updates on the two Task Forces

Talks (10 min in total), focus on FAIR for ML in different contexts. Tentative list:

Rationale for and Significance of FAIR4ML

Potential Approaches for FAIR4ML

What do we learn from comparison to FAIR in Data and Software

Part 2: 

Review the metadata schemas for ML

Model and dataset cards

ML-related platforms with some support for metadata

Continue the discussion around the FAIR principles for ML Determining the primary goal of a FAIR4ML White paper (30 min)

Review potential new Task Forces that can be initiated 

Review of actions and Wrap up

Additional links to informative material
FAIR4ML IG page: https://www.rd-alliance.org/groups/fair-machine-learning-fair4ml-ig 
Previous plenary sessions: https://www.rd-alliance.org/defining-fair-machine-learning-ml  and https://www.rd-alliance.org/plenaries/rda-20th-plenary-meeting-gothenburg-hybrid/defining-roadmap-towards-fair-machine-learning

Avoid conflict with the following group (1)
FAIR for Virtual Research Environments WG

Brief introduction describing the activities and scope of the group
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 speakers and attendees in this session are those 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. Finally, we had two BoF sessions (in VP18 and VP19) towards establishing a critical mass of interested parties and drafting an IG Charter document.
Ultimately, the FAIR for Machine Learning Interest Group was formally accepted in September 2022, and had the first formal meeting during the RDA P20.

Estimate of the required room capacity
30-50

I Understand a Chair Must be Present at the Event to Hold the Breakout Session
Yes

Meeting objectives
 
Click here for the collaborative session notes
 
The main focus of the meeting is to continue the work on the activities of the FAIR4ML Interest Group. Specifically, the discussion will be focused on the following objectives:

Advance the consensus around ML-relevant metadata:

Gathering a collection of models and initiatives for representing metadata in Machine Learning platforms / models.

Creating crosswalks between different metadata models for ML

Continue the work on the definition of FAIR for ML, in the context of a white paper

Define and prioritize cases for new Task Forces and Working Groups

Ultimately, the outcome of this meeting will be a set of concrete actions for the next 6-12 months, including building a community of practice for information sharing about ML and FAIR pertaining to ML.
In order to facilitate the discussion, people from relevant activities, initiatives and projects will be invited to offer their perspective. Such initiatives will include the Pistoia Alliance, ELIXIR, the CLAIRE network, NFDI4DataScience, and FAIR4HEP among others. FAIR4ML will actively pursue the identification and engagement with additional relevant groups. Given the hybrid form of the Plenary, some of these perspectives may be delivered as pre-recorded videos.

Please indicate at least (3) three breakout slots that would suit your meeting.
Breakout 2, Breakout 3, Breakout 4

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