This panel will explore what makes a machine learning model, or an “AI system”, Free Software / Open Source. Some topics we may address: - What, if anything, is the source code of a model? If the source code includes training data, what are the implications for LLMs and other large generative models? Can trained weights themselves be considered source code? - What does user freedom (and in particular, freedom to study and modify) mean in the context of AI models? How, if at all, does it differ from our traditional understanding of software freedom? - Are there any reasons to tolerate use restrictions in AI model licenses that we would reject for FOSS? - Proposed normative definitions of free/open AI and related efforts, including the OSI’s OSAID 1.0, FSF’s criteria for free machine learning applications, and the Model Openness Framework - Should we have different tiers of free-ness/openness when assessing AI models/systems? - Leaving aside the question of source code, are there other kinds of artifacts that should be released with a model for it to legitimately be considered free/open?