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arXiv · 2510.10732

When Openness Fails: Lessons from System Safety for Assessing Openness in AI

Tamara Paris·Shalaleh Rismani
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Abstract

Most frameworks for assessing the openness of AI systems use narrow criteria such as availability of data, model, code, documentation, and licensing terms. However, to evaluate whether the intended effects of openness - such as democratization and autonomy - are realized, we need a more holistic approach that considers the context of release: who will reuse the system, for what purposes, and under what conditions. To this end, we adapt five lessons from system safety that offer guidance on how openness can be evaluated at the system level.

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Tamara Paris, Shalaleh Rismani. 2025-10-12. When Openness Fails: Lessons from System Safety for Assessing Openness in AI. https://arxiv.org/abs/2510.10732

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