AI Supply Chain Galaxy: 3D Visual Analytics for License Compliance
2026-06-15 • Software Engineering
Software EngineeringArtificial Intelligence
AI summaryⓘ
The authors created AISCG, a 3D tool to help understand and check compliance in the complex networks of reused AI models. This system visually maps how models are connected and helps find issues like missing or conflicting licenses. They tested AISCG on over 900,000 models and found many had legal risks or data gaps. Using a detailed example with Llama models, they showed how their tool makes tracing rule violations easier and less confusing for auditors.
machine learning modelsmodel reusecompliance auditingmodel provenancelicense management3D visualizationHugging Facelineage tracingecosystem analysisAI supply chain
Authors
Weiru Han, Xuetao Shi, Wenyi He, Wei Wang, Rui Zhao, Moming Duan
Abstract
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.