12 FEB 2024
Group(s) submitting the application:
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:
- Review and discuss the effort around the ML Lifecycle, for example, publishing of the report,challenges and opportunities in applying FAIR principles to stages of the lifecycle and adoption of the lifecycle in the Skills4EOSC project evaluating and recommending FAIR practices for AI research
- Review and discuss the effort around metadata for ML
- 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 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:
- Welcome (3’)
- Introduction to the FAIR4ML IG and how to join (5’)
- Output of the TF 1: Presentation and discussion on the ML Lifecycle for FAIR4ML (30’)
- Overview, including how to get involved (10’)
- Feedback (20’)
- Output of the TF2: Presentation and discussion of a ML model metadata based on schema.org (30’)
- Overview, including how to get involved (10’)
- An annotated example (5’)
- Feedback (15’)
- Possible new activities (flash talks, 5’ each) (20’)
- For example, proposals of new activities for the existing Task Forces or proposals for new Task Forces. A maximum of four flash talks will be included in the agenda.
- Confirmed talks
- ML Commons community and Croissant specification for Datasets
- Next actions and wrap-up (2’)
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:
- 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
- Reproducibility Interest Group
Group chair serving as contact person:
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 schema.org-based 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: 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):
Avoid conflict with the following group (2):
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?:
Yes