Which checks should I run on an approval sample before using it?
A checklist of seven checks, from recomputing validity labels to perturbation tests and a grounding review, to run on a small batch before any training or evaluation use.
Before training on or evaluating with a sample, run these checks on a small batch and keep the results for the discussion with the supplier.
- Recompute each approval's validity from the role, grant, limit table and policy version, and compare it with the supplied label. - Confirm every identifier in an event resolves to an existing request, role and delegation, with dates in a sensible order. - Count near-limit requests, lapsed delegations, second-approver cases, rejections and escalations to see that the set is not all clean approvals. - Perturb an amount or date on a few tasks and check that the expected outcome changes correctly. - Have an approvals practitioner read a handful of notes and cases for plausibility. - Check that the held-out split differs by policy variant or organisation, not only by row. - Search samples for text that looks too specific to be generated, and record any grounding and disclosure concerns.
A short, repeatable set of checks turns a sample into evidence you can bring to the supplier discussion.
- Audience:
- Model labs
- Content type:
- Checklist
- Buyer question:
- How to evaluate it
Content release: r-a220eb6e099b243b
Related questions
- Which failure modes should an approval agent evaluation include?
- How do I inspect the quality of generated authority rules and approval cases?
- How do environment reset, state diffs and grader hooks work for approval tasks?
- What success criteria should I use for an agent handling approvals?
- How does an evaluation catch an agent approving its own requester's credit adjustment?
- What checks should I run before accepting a generated approval sample?