If you cannot attest what went into a model, you cannot defend what comes out of it. Provenance is not paperwork. It is the difference between a system you can stand behind and one you are quietly hoping nobody examines.
Most artificial intelligence systems carry a hidden liability: nobody can prove what data trained them. I argue that data provenance and poisoning are the real exposure, that you cannot defend outputs you cannot trace to inputs, and that the only honest answer is a signed, hash-chained record you can verify offline without trusting the vendor.
Originally published on mickai.co.uk. This is a cross-post; the canonical version, with the full body, footnotes and references, lives on the mickai.co.uk article page.
Top comments (0)