Event details

Online

This month, we are pleased to welcome special guests Leyla Jael Garcia-Castro (ZB MED, Germany), alongside colleagues from the FAIR4ML project Daniel Garijo (UPM, Spain) and Dan Katz (NCSA and UIUC, USA) who will present “Research Data Alliance & FAIR4ML, a vocabulary to describe AI models."

Abstract

In recent years, we have seen an increase in the creation and usage of Artificial Intelligence-based models in most, if not all, scientific disciplines. AI models have become research artifacts themselves, with the need to provide rich descriptions and documentation to facilitate reuse, transparency, and reproducibility, while also adhering to common good practices for research. Despite their importance and applicability (e.g., predictions, classification, clustering), machine-readable metadata describing these models is not often provided. Some efforts such as the ML Model Cards and the Data, Optimization, Model, and Evaluation recommendations promote good reporting practices. However, there is still a gap when it comes to support rich structured semantic metadata as descriptions are still buried in text-based documents such as readmes, tables, reports and scholarly articles. To overcome this challenge, the Research Data Alliance (RDA) FAIR for Machine Learning Interest Group (FAIR4ML-IG) took over the task to create FAIR4ML, an extension of the schema.org vocabulary aligned to Croissant ML, designed to describe AI model metadata. In this talk we will introduce the schema and present two use cases of early adopters.