How do I inspect the quality of a generated conditional approval dataset?

Inspecting a conditional-approval dataset is mostly about checking whether the state is coherent and whether the hard cases are actually hard.

Start with structural checks. Every condition should link to one approval, every evidence row to one condition, and timestamps should run in a plausible order. A gate should not be open while a mandatory condition is unsatisfied unless a waiver exists and cites a role with authority. These can be tested by script across a whole release.

Then check policy consistency. Read a sample of cases against the supplied policy text and ask whether the labelled outcome follows. Look for cases where the label depends on information missing from the records.

Next, look at spread. A release that contains only clean cases will not test anything. Ask for the proportion of cases with partial evidence, late evidence, added conditions, expiry and pressure to proceed, and read examples from each group.

Finally, inspect grounding and disclosure risk. The data is generated, so ask how the scenarios were derived from observed operating practice, check whether any detail looks traceable to a specific organisation, and judge whether the vocabulary and handover habits match how practitioners in that domain write.

Quality here is something you check in a sample, not something to assume.