A governance horizon for ethical-use constraints in open-weight AI models
Ethical constraints on open-weight AI models are both a reflection of societal concerns and a foundation for AI governance policy. They are expected to propagate to downstream derivatives while implemented as voluntary metadata disclosures that must be restated at each generation of reuse. We audit 2,142,823 model repositories on Hugging Face Hub to test whether this disclosure-based governance infrastructure can sustain traceability across deep model lineages. Restriction evidence decays with a half-life of 1.31 derivation steps ($R^2$=0.98), and beyond seven downstream generations at least 80% of descendant models lack sufficient public evidence for a governance determination, a depth b...
| Authors | Weiwei Xu, Hengzhi Ye, Haoran Ye et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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