Skip to main content


The new RDA web platform is still being rolled out. Existing RDA members PLEASE REACTIVATE YOUR ACCOUNT using this link: Please report bugs, broken links and provide your feedback using the UserSnap tool on the bottom right corner of each page. Stay updated about the web site milestones at

12 FEB 2024

By Leyla Jael Castro

Collaborative session notes

Group(s) submitting the application: 

FAIR for Machine Learning (FAIR4ML) IG

Meeting objectives: 

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:

Ultimately, the outcome of this meeting will be a set of concrete actions for the next 6-12 months, including community spaces, e.g., building a community of practice, for information sharing about ML and FAIR pertaining to ML.

FAIR4ML will actively pursue the identification and engagement with additional relevant groups. Given the virtual form of the plenary, some of the session talks may be delivered as pre-recorded videos.

Meeting agenda:  

Target Audience: 

Researchers and Data Professionals interested in developing and/or deploying Machine Learning solutions, focusing on how the FAIR principles can potentially be interpreted in and applied to 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:

Group chair serving as contact person: 

Leyla Jael Castro

Brief introduction describing the activities and scope of the group: 

With the explosion of Machine Learning models and the fast-becoming ubiquitous use of Artificial Intelligence, trust in predictions and results is crucial. Guidelines on how to implement FAIR for Machine Learning is one promising research direction to foster this trust. Over the past 10 years, there is a large amount of FAIR work, both in RDA and elsewhere, initially focused on data and now also on software and other products but generally not on ML models. With the aim of filling this gap, the FAIR for Machine Learning Interest Group was formally accepted in September 2022 after about 2 years of initial landscaping and community-building. It currently comprises two task forces, Task Force 1 working on a FAIR ML lifecycle and relevant elements to increase the FAIRness of the the different bits, and Task Force 2 working on a metadata schema to represent ML models and their connections to e.g., data and software.

The FAIR4ML IG has a regular monthly meeting, on the fourth Monday of the month alternating between 08:00 UTC and 20:00 UTC (adjusted according to the European Summer) to accommodate multiple time zones.

Short Group Status: 

Recognised & Endorsed from 2023.

Type of Meeting: 

Working meeting

Additional links to informative material: 

FAIR4ML IG page: 

Previous plenary sessions:  and

Avoid conflict with the following group (1): 

Data Discovery Paradigms IG

Avoid conflict with the following group (2): 

RDA & ReSA: Policies in Research Organisations for Research Software (PRO4RS)

Meeting presenters: 

FAIR4ML Co-chairs ((Fotis Psomopoulos, Daniel S. Katz, Daniel Garijo, Beatriz Serrano-Solano, Leyla Jael Castro, Anne Fouilloux, Curtis Sharma, Gnana Bharathy, Line Pouchard)) + Volunteers for leading the discussion and breakout sessions

Are you willing to host a second, repeat, session at a different time zone?: