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The rights boundary of fine-tuning data: are you actually allowed to train on it?
Fine-tuning quality depends on data, and that data's licensing, privacy, and provenance decide whether you can use it with confidence.
Fine-tuning tailors a model to your business, but data cuts both ways: whether the corpus is licensed, contains personal information, or mixes in copyrighted or third-party confidential content all affect whether you may lawfully use it.
In particular, using user data for fine-tuning is usually the 'improve the model' purpose, which typically needs separate, revocable consent rather than being buried generically in a privacy policy.
The pragmatic approach is a data-provenance and licensing ledger, minimization and masking of sensitive and personal data, and a data-to-model-version mapping so you can account for it in a challenge or deletion request.