An open, model-agnostic "interaction card": verifiable provenance for AI decisions

The author of an open, work-in-progress format called "Causal Seal" proposes a model-agnostic "interaction card" that binds a SHA-256 fingerprint to an AI output, its causal parameters (model identity, constraints, context, timestamp), and the moment of emission, enabling verification without access to the model. The format is open (CC BY 4.0), vendor-neutral, and available via `pip install causal-seal`, with a browser verifier, aiming to complement model cards by documenting individual decisions rather than the model itself.

I’m the author of an open, work-in-progress format for the provenance of AI decisions, and I’d love feedback from this community — model cards are, in a sense, the ancestor of what I’m building, and HF champions open, model-agnostic documentation. The gap. Model cards document the model . There’s still no open way to verify how a specific output was produced . The idea. A “Causal Seal” attaches to a single output a small record that binds — into a SHA-256 fingerprint — the output, the causal parameters that governed its generation model identity, constraints, context, timestamp , and the moment of emission. Anyone can verify it with no access to the model or any private system. It’s open CC BY 4.0 and privileges no vendor. Think of it as the per-decision, verifiable counterpart to a model card — an “interaction card.” Complementary, not competing. What already exists public, reproducible : pip install causal-seal and a browser verifier My questions: Any feedback is welcome — this is early, single-maintainer, open work.