[$] MOT: a tool to fight openwashing in AI
The Model Openness Tool (MOT) aims to clarify the openness of large language models (LLMs) amidst concerns of openwashing. Arnaud Le Hors discussed the challenges of assessing model openness at the Open Source Summit North America 2026. The tool is part of a broader effort to establish clearer definitions and frameworks for evaluating the openness of AI models.
- ▪Many LLMs are labeled as open source, but often do not meet the Open Source Initiative's criteria.
- ▪The Model Openness Framework (MOF) categorizes models into three classes based on their openness and completeness.
- ▪Restrictions in model licenses can lead to legal risks for users who assume they can freely use and modify downloaded models.
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Did you know...? LWN.net is a subscriber-supported publication; we rely on subscribers to keep the entire operation going. Please help out by buying a subscription and keeping LWN on the net. By Joe BrockmeierMay 27, 2026 OSSNA Many large language models (LLMs) are described as open source, but if one looks a bit deeper it turns out that is not actually so; the model may be free to download, it may be "open weight", but it does not fit the Open Source Initiative (OSI) Open Source Definition (OSD). Assessing the actual openness of models is not easy, as Arnaud Le Hors explained in his talk about the Model Openness Tool (MOT) at Open Source Summit North America 2026.
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