Position: No Retroactive Cure for Infringement during Training

摘要

As generative AI faces intensifying legal challenges, the machine learning community has increasingly relied on *post-hoc mitigation*---especially machine unlearning and inference-time guardrails---to argue for compliance. **This paper argues that such post-hoc mitigation methods cannot retroactively cure liability from unlawful acquisition and training, because compliance hinges on data lineage, not the outputs.** Our argument has three parts. First, unauthorized copying/ingestion can be a legally complete *completed act*, and model weights may operate as *fixed copies* that retain training-derived expressive value, making later filtering beside the point for infringement. Second, *contract* and *tort/unfair-competition* rules---via licenses, terms of service, and anti-free-riding principles---can independently restrict access and use, often bypassing copyright defenses (e.g., fair use or TDM exceptions). Third, since value from protected inputs can persist in weights, remedies such as *unjust enrichment* and *disgorgement* may require stripping gains and, in some cases, reaching the model itself. We therefore argue for a shift from *Post-Hoc Sanitization* to verifiable *Ex-Ante Process Compliance*.