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.

翻译：生成式人工智能（GAI）带来了前所未有的可能性，但其商业化引发了关于透明度、可复现性、偏见和安全性的担忧。许多“开源”GAI模型缺乏完全理解与复现的必要组件，部分模型采用限制性许可协议，这种做法被称为“开源洗白”。我们提出模型开放框架（MOF），这是一个遵循开放科学、开源、开放数据和开放获取原则，基于完整性与开放性对机器学习模型进行分级分类的评估体系。MOF要求模型开发生命周期中的特定组件必须包含在内，并在适当的开放许可协议下发布。该框架旨在防范宣称“开放”的模型被错误表述，指导研究人员和开发者以宽松许可协议提供所有模型组件，并帮助公司、学术界和爱好者识别可安全采用且无限制的模型。MOF的广泛推广将培育更开放的AI生态系统，加速研究、创新与模型应用。