{"id":146,"title":"FAIR for Machine Learning Models","url":"https://www.nfdi4datascience.de/","description":"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.","image":"","tags":"FAIR,  machine learning model, FAIR assessment, NFDI4DataScience","type":"","license":"https://creativecommons.org/licenses/by/4.0/","authors":[1287,1411],"metrics":[611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637]}