The Model Openness Framework (MOF) is a proposed system that rates machine learning models on their openness and completeness. Developed by researchers from LF AI Data, Generative AI Commons, Linux Foundation, University of Oxford, Columbia University, and IBM, the MOF aims to prevent misrepresentation of models claiming to be open, guide researchers in providing all model components under permissive licenses, and help identify models that can be safely adopted. The MOF is particularly important for Generative AI (GAI), addressing concerns about transparency, reproducibility, bias, and safety. The framework also tackles the issue of openwashing, where models are promoted as open-source but lack an open-source license.
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