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Evidence record 12002 · automatically gathered

A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models

Pretrained machine learning (ML) models help developers build ML-intensive software systems without training models from scratch. However, model repositories often provide incomplete machine-readable documentation about model provenance, licenses, datasets, limitations, and external references, creating transparency and governance gaps across the AI supply chain. Artificial Intelligence Bills of Materials (AIBOMs) address these gaps by documenting AI artifacts, including models, metadata, licens

Record details

Published: 19 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Transparency
Retrieved: 21 July 2026

source-onlyevidence status

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How to cite this record

ethics.ai (19 July 2026), “A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models,” evidence record 12002, https://ethics.ai/record/12002 (originally published by arXiv).

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