Supply Chain Transparency

Supply chain transparency in AI refers to the practice of documenting and disclosing where the components of an AI system come from, including training datasets, pre-trained models, third-party code libraries, and the vendors who supply them. For enterprise governance teams, it matters because regulators and auditors increasingly expect organizations to show what went into a system they deploy, and gaps in that record can create legal, contractual, and reputational exposure. Tracking this information through tools such as AI bills of materials, which are inventories listing each component and its origin, helps risk managers answer basic questions about licensing, data provenance, and known vulnerabilities.

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ResearchGlobal2026-10-26

AI Training Labor Map Links 13 Vendors and 31 Countries, Exposing Supply Chain Gap

A Brown University undergraduate has published an interactive map connecting 13 data labeling companies, 141 corporate customers, and 31 countries. The map exposes the hidden labor supply chain behind AI training data. The project documents low-wage annotation work concentrated in the Global South, which the researcher frames as intentionally opaque. For compliance teams, the map creates a citable record of supply chain conditions that vendor due diligence programs have rarely reached.

supply chain transparency AI labor vendor due diligence training data human rights ESG disclosure