Scan a model card
Paste any AI system’s documentation and see which of the seven governance dimensions it addresses and which it is silent on. This is the same automated scan that produces the card figures on the leaderboard, run against a document of your choosing instead of one of ours.
This runs in your browser. The text is not uploaded, because there is nothing here to upload it to — no request is made when you press the button, which you can confirm in your browser’s network tab.
What this measures, and what it does not
It reads a document, not a system
Every one of the 25 checks answers a question about the text — does it state prohibited uses? does it name an evaluation dataset? — and none of them measures whether the system is any good. A thorough card on a poor model scores well, and it should: what is being measured is what was written down.
It is capped at T2
A card is a vendor-published artifact by definition, so nothing read from one can reach T3 (independently reproducible) or T4 (independently verified). The composite therefore cannot exceed 50, and the percentage above is a percentage of that ceiling — not of a full assessment. A card scan and a GRI assessment are different scales and do not compare.
It scores under the general profile only
A GRI without its profile is meaningless, and weighting profiles live in the database, which a page running in your browser cannot reach. So this uses flat weighting — every dimension equal — which is what the leaderboard uses for card scans too. The method sets out the profiles that reorder a ranking.
A low score is often a real finding
Across the 7 dimensions, the cards in this registry are routinely silent on fairness and on regulatory mapping. If your document scores badly there, it is in the majority — which is the argument for fixing it, not for discounting the measurement.