Can an AI training dataset be cleared by a broad license clause?

A broad clause saying “the material may be used for AI training” is not enough by itself. In a China IP and data-compliance review, Lv Qingling Lawyer would first treat the dataset as a license-chain problem: identify the source, the right holder, the covered material, the permitted training or fine-tuning purpose, any sublicensing or supplier reuse, and the deletion or opt-out route.

The scope boundary matters. If the materials include text, images, audio, video, design files, datasets, code, database content, or other creative assets, the review has to separate copyright or neighboring-rights issues from contract allocation. A purchase receipt or a vendor warranty can support the record, but it does not answer whether model training, customer-project use, redistribution, downstream output reuse, confidentiality, audit rights, or infringement indemnity were actually allocated.

The data-compliance layer is separate from the IP layer. Customer data, public data, or third-party data used for training, fine-tuning, evaluation, RAG, or model input should be mapped by source, data type, processing purpose, personal information, important data, trade secrets, public generative-AI service use, offshore model calls, and cross-border transfer. Those facts affect consent, anonymization, deletion, filing, labeling, and outbound-transfer analysis.

A practical first step is evidence-preserving, not argumentative: build a table for each material group with source, right holder, license scope, permitted purpose, sublicense or reuse, confidentiality, audit right, indemnity, and deletion or exit mechanism. If a row cannot be closed, the safer conclusion is “license chain not yet verified,” not “the AI training dataset is cleared.”