I am building a local AI system around a distinction I think gets lost in the training-data debate. The system has one strictly governed evidence library: public-domain or explicitly permitted sources only, used when it needs to quote, cite, or ground a user-facing answer. Separately, I am exploring an abstraction layer. The intended output is not chunks, embeddings that recover passages, or a source substitute. It is a compact original representation of facts, causal relationships, and procedures. Source text is discarded; the abstraction is tested for reconstruction and close-paraphrase leakage. The human analogy is simple: someone reads a book, learns an idea, and later applies the idea without copying the book. The machine distinction is harder because ingestion itself can create technical copies. My question is not “is all AI training fair use?” It is narrower: does an architecture that deliberately prevents source retention, retrieval, imitation, and close output materially change the ethical or legal analysis? What would a serious technical standard for that boundary require? https://preview.redd.it/bxqo21f4qmlh1.png?width=1080&format=png&auto=webp&s=72dae16b949d9de156276f25230bb8739f84faaa submitted by /u/HotEstablishment7184
Originally posted by u/HotEstablishment7184 on r/ArtificialInteligence
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