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, which includes aspects such ase:
challenges and opportunities in applying FAIR principles to the stages of the lifecycle
alignment to the lifecycle proposed 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
This discussion will include progress on the two existing task forces since RDA virtual plenary 22.
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.

Welcome (3’)
Introduction to the FAIR4ML IG and how to join (5’)
Output of TF 1: Presentation and discussion on the ML Lifecycle for FAIR4ML (30’)
Overview of activities so far
ML Life Cycle
Mapping of FAIR across the Life Cycle
Structure of white paper
Feedback and how to get involved (20’)
Output of TF2: Presentation and discussion of a ML model metadata based on schema.org (30’)
Overview of produced output (10’)
Vocabulary (https://w3id.org/fair4ml)
Cross-walks
Feedback and how to get involved (20’)
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.
Possible talks?

Next actions and wrap-up (2’)

Researchers and Data Professionals interested in developing, deploying, sharing, and/or supporting Machine Learning solutions, focusing on how the FAIR principles can be interpreted in and applied to the context of ML to improve the development, deployment, sharing, and use of such models.
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:
Software Source Code Interest Group, in part as the maintenance home for the FAIR for Research Software Working Group (FAIR4RS) outputs
FAIR Digital Object Fabric Interest Group
FAIR Data Maturity Model Working Group
Reproducibility Interest Group
Artificial Intelligence and Data Visitation (AIDV) WG