Abstract: Generative AI (GAI) offers unprecedented possibilities but its
commercialization has raised concerns about transparency, reproducibility,
bias, and safety. Many "open-source" GAI models lack the necessary
components for full understanding and reproduction, and some use
restrictive licenses, a practice known as "openwashing." We propose the
Model Openness Framework (MOF), a ranked classification system that rates
machine learning models based on their completeness and openness,
following principles of open science, open source, open data, and open
access. The MOF requires specific components of the model development
lifecycle to be included and released under appropriate open licenses. This
framework aims to prevent misrepresentation of models claiming to be
open, guide researchers and developers in providing all model components
under permissive licenses, and help companies, academia, and hobbyists
identify models that can be safely adopted without restrictions. Wide
adoption of the MOF will foster a more open AI ecosystem, accelerating
research, innovation, and adoption.