AI Supply Chain Galaxy: 3D Visual Analytics for License Compliance
For AI compliance auditors and researchers, this system reduces cognitive load in navigating complex dependency networks to identify license violations.
The paper presents AI Supply Chain Galaxy (AISCG), a 3D visual analytics system for auditing license compliance in AI model supply chains. Analyzing 908,449 Hugging Face models, it found 55.46% with compliance risks, including 56.67% license omission in adapters and 8.05% license drift in fine-tuning.
The rapid proliferation of machine learning model reuse has transformed the AI ecosystem into a highly interconnected supply chain. Traditional compliance tools and static reports struggle to navigate these massive, multi-hop dependency networks. To address this, we present AI Supply Chain Galaxy (AISCG), an interactive 3D visual analytics system for model provenance and compliance auditing. AISCG maps models into a 3D spatial layout, integrating explicit structural dependencies with a rule-based compliance engine. It supports multi-scale exploration, from global community detection to localized, path-aware lineage tracing. We demonstrate its efficacy through an ecosystem-scale empirical analysis of 908,449 models from Hugging Face. Our findings reveal a concerning landscape: 55.46% of models exhibit compliance risks or metadata conflicts/omissions. We also identified distinct risk patterns, including a 56.67% license omission rate in adapter derivations and an 8.05% "license drift" rate in fine-tuning. Through a case study on the complex Llama model family, we show how AISCG empowers analysts to intuitively trace inherited restrictive terms and identify root causes across deep topological networks, significantly reducing the cognitive load of compliance auditing.