How do you make AI output verifiable in regulated software?
In regulated software, a model’s output has to be traceable to its sources so a reviewer and an auditor can check it. The interface shows which records, documents or code the output relied on and which checks and limitations apply.
Explanation here means evidence a person can verify.
Key facts
- Show a confidence measure only when its meaning and reliability have been validated for the task. Otherwise show evidence, missing inputs and checks still required.
- The model and version should be stored with the output.
- The reviewer and the time of review should be recorded.
- A reviewer also needs to see what the model did not check.
- Generic explanations that point to no data do not make output verifiable.
What to show
| Element | Why it is needed |
|---|---|
| Links to source records | The reviewer checks the output against its sources |
| Confidence | If validated for this task, explain the measure and how it guides review |
| Model and version | An auditor can tell which model produced a value |
| Reviewer and time | The record shows who accepted the output |
| What was not checked | The reviewer knows the limits of the output |
In regulated work, a value has to be defensible months later in front of someone who was not in the room. An output without sources cannot be defended, however accurate it was. Verifiability is therefore part of the feature, and it affects whether regulated buyers accept the product at all.
A source link alone does not prove a conclusion. Connect each material claim to relevant evidence and its version, distinguish generated interpretation from source facts, and record unresolved contradictions or missing checks.
How it shows up in the product
- An evidence panel next to each result
- References that open the exact record, document or line of code
- Validated uncertainty measures, where available, with their meaning and limitations
- Views for different audiences over the same evidence
Options and trade-offs
- Showing every source for every output makes screens heavy, so many products show sources on demand and accept one more click per check.
Common mistakes
- Generic explanations that point to no data
- Hiding low confidence to keep the screen clean
- No way to see what the model did not check
- Model output stored without the model and version
Related experience