Model Cards and Documentation
A short structured record of what a model is for, how it was built, and where it fails makes responsible use possible.
Documenting the Model Itself
A model card is a concise, structured document describing a model: its intended use, its training or construction, its performance, and, crucially, its limitations and failure modes. The idea, developed for machine-learning models, generalizes to any model whose users need to know where it can and cannot be trusted.
What a Card Records
- Intended use and out-of-scope use.
- How it was built: data or equations, and key assumptions.
- Performance, broken down by condition rather than a single headline number.
- Known limitations, biases, and failure modes.
- The valid input domain, beyond which behavior is undefined.
Why the Limitations Section Matters Most
Most misuse of a model comes from applying it outside its intended scope, treating a tool built for one regime as valid everywhere. A card that states out-of-scope uses and known failure modes does more to prevent harm than one that only advertises strengths. Honest documentation of limits is what makes a model safe to hand to someone else.
Performance Is Not One Number
A single accuracy figure hides where a model fails. A good card reports performance across the conditions that matter, different regimes, subgroups, or input ranges, so a user can see whether it works for their case specifically rather than on average. Aggregate metrics can be strong while the case at hand is exactly where the model breaks.
In Scientific Modeling
The scientific analog is a clear statement of a model's governing assumptions, validation domain, and known discrepancies. Kronos design work states which physical effects a model includes, the regime in which it was validated, and the open reconciliations still outstanding, so a result carries the context needed to use it responsibly.