FAIR Principles Implementation in ML/AI - Findings from Skills4EOSC Delphi Study

Abstract

Implementing the FAIR (Findable, Accessible, Interoperable, and Reusable) principles for scientific data management in machine learning (ML) and artificial intelligence (AI) offers numerous benefits, including higher model reliability, more collaborative research, and greater reproducibility. Despite these advantages, there is a lack of clear, practical guidelines for improving the FAIRness of ML/AI outputs, especially the models. To address this gap, Skills4EOSC Task 6.3.3a of Work Package 6: Professional Networks for Lifelong Learning conducted a Delphi Study to gather expert consensus on implementing FAIR principles in ML/AI model development. A Delphi study, involving two rounds of surveys followed by an online meeting, was conducted. In the first round, ML/AI experts from Europe and beyond rated suggested FAIR practices and proposed additional ones. The second round involved feedback and re-evaluation of these practices. The final meeting included detailed discussions on the Top 10 practices for FAIR principles implementation in ML/AI. The resulting Top 10 practices aim to provide guidelines for researchers and data management professionals to implement FAIR principles in ML/AI.