Building towards FAIR for Machine Learning

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08 Jun 2023
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Meeting objectives: 


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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:

  1. 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

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

  3. 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.

Meeting agenda: 

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

Target Audience: 

Researchers and Data Professionals interested in Machine Learning, and primarily in how the FAIR principles can potentially be interpreted in the context of ML.

Particularly relevant are members of relevant RDA groups with complementary focus, in order to identify potential synergies early on. An initial list of these key groups is:

  • FAIR for Research Software Working Group (now complete, but the RDA entity which will maintain the WG output is the SSC IG)

  • Software Source Code Interest Group

  • FAIR Digital Object Fabric Interest Group

  • FAIR Data Maturity Model Working Group

Group chair serving as contact person: 
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 - at RDA VP16 to start discussion on this at RDA, with a dedicated BoF session at RDA VP17 ( 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.

Short Group Status: 

Starting work and task forces

Type of Meeting: 
Working meeting
Avoid conflict with the following group (1): 
Avoid conflict with the following group (2): 
Meeting presenters: 
Fotis E. Psomopoulos, Daniel S. Katz, Leyla Jael Castro, Daniel Garijo, Beatriz Serrano-Solano, Anne Fouilloux, Curtis J. M. Sharma, Gnana Bharathy, Line Pouchard