[$] MOT: a tool to fight openwashing in AI

The Model Openness Tool (MOT), presented by Arnaud Le Hors at Open Source Summit North America 2026, aims to help users assess the true openness of large language models (LLMs) that are often misleadingly labeled as open source. The tool evaluates models against the Open Source Initiative's Open Source Definition to combat "openwashing," where models are described as open but only offer limited access such as open weights. MOT provides a standardized way to determine the degree to which an LLM meets open-source criteria.

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 https://opensource.org/ai/open-weights ", but it does not fit the Open Source Initiative http://opensource.org/ OSI Open Source Definition https://opensource.org/osd OSD . Assessing the actual openness of models is not easy, as Arnaud Le Hors explained in his talk about the Model Openness Tool https://mot.isitopen.ai/ MOT at Open Source Summit North America https://events.linuxfoundation.org/open-source-summit-north-america/ 2026. The tool is designed to help users of LLMs understand to what degree a model is or is not open, and to combat the openwashing https://openwashing.org/ that is prevalent with LLMs.