FAIR for Machine Learning Models

This rubric consists of assessment metrics that evaluate the FAIR maturity of ML models. The metrics are proposed based on relevant and well-established initiatives. The metrics of this rubric rely on a hybrid assessment method since they contain both manual and automated assessment metrics. In this way, the results from the automated (conducted via F-UJI) and the manual assessments are included in the same (FAIRshake) evaluation rubric and form the overall FAIR assessment score for an ML model.

License: https://creativecommons.org/licenses/by/4.0/

Tags: FAIR machine learning model FAIR assessment NFDI4DataScience

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Assessment Metrics Date
Target Project   Global, unique identifier Persistent identifier ML model versions Core descriptive elements ML model identifier in the metadata FAIR Metadata Access conditions Standards protocols for metadata access Standard protocols for data access Model authentication and authentication Metadata preservation Formal knowledge representation language Semantic resources Qualified references ML model content specification License information Provenance information ML model type Dataset training, validation and testing ML model dataset collection ML model source code ML model hyperparameters ML model training ML model evaluation References to other ML models ML model metadata standards ML model file format