Generative AI (GAI) offers unprecedented opportunities for research and innovation, but its commercialization has raised concerns about transparency, reproducibility, and safety. Many open GAI models lack the necessary components for full understanding and reproducibility, and some use restrictive licenses whilst claiming to be ``open-source''. To address these concerns, 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 individuals and organizations identify models that can be safely adopted without restrictions. By promoting transparency and reproducibility, the MOF combats ``openwashing'' practices and establishes completeness and openness as primary criteria alongside the core tenets of responsible AI. Wide adoption of the MOF will foster a more open AI ecosystem, benefiting research, innovation, and adoption of state-of-the-art models.

翻译：生成式人工智能（GAI）为研究与创新带来了前所未有的机遇，但其商业化进程引发了关于透明度、可复现性与安全性的担忧。许多公开的GAI模型缺乏实现全面理解与可复现性所必需的组件，部分模型在声称“开源”的同时却采用限制性许可协议。为解决这些问题，我们提出模型开放性框架（MOF），这是一个遵循开放科学、开源、开放数据与开放获取原则的等级分类体系，依据机器学习模型的完整性与开放性对其进行分级评估。MOF要求模型开发生命周期中的特定组件必须被包含在内，并在适当的开放许可协议下发布。该框架旨在防止对声称开放的模型进行不实表述，指导研究者与开发者提供所有采用宽松许可的模型组件，并帮助个人与组织识别可安全无限制采用的模型。通过促进透明度与可复现性，MOF抵制“开放性粉饰”行为，并将完整性与开放性确立为负责任人工智能核心原则之外的首要标准。MOF的广泛采用将培育更开放的人工智能生态系统，促进前沿模型的研究、创新与应用。