AI compliance testing that produces audit-ready evidence
An accurate model can still treat groups unfairly or keep data it should not. We test fairness, explainability and data handling, and package the evidence.
The whole story, short
The problem
High overall accuracy hides gaps for specific groups.
What we did
- Test bias and fairness across groups
- Check explainability
- Test PII and PHI handling
- Validate human-in-the-loop controls
- Produce audit-ready documentation
- Add ongoing monitoring tests
The outcome
Audit-ready evidence package
What goes wrong
- High overall accuracy hides gaps for specific groups.
- Individual decisions cannot be explained to a reviewer or applicant.
- Sensitive data ends up in logs and debug output.
- Evidence is scattered, so approvals drag on for months.
Why the usual fix falls short
Accuracy metrics do not reveal bias, and compliance teams need traceable evidence, not a slide that says the model was tested.
How we tested it
Test bias and fairness across groups
Compare approval and error rates across groups, including combinations of groups.
Check explainability
Test whether individual decisions can be explained in terms a reviewer can verify and an applicant can understand.
Test PII and PHI handling
Trace personal and health data through training, logs and reports.
Validate human-in-the-loop controls
Confirm borderline and adverse decisions reach a person with enough information to overrule the model.
Produce audit-ready documentation
Organize data, tests, results, fixes and sign-offs into a traceable package.
Add ongoing monitoring tests
Schedule fairness and drift checks so the evidence stays current after launch.
Questions buyers ask
What is AI compliance testing?
AI compliance testing checks an AI system against the legal, regulatory, and customer requirements that apply to it, then records the evidence. It commonly covers fairness, explainability, data handling, and human oversight. It supports a compliance program, but it does not replace legal advice or a formal certification.
How do you test AI for bias?
Compare the model's outcomes and error rates across relevant groups, using fairness metrics suited to the decision. Test with representative data and include combinations of groups, since gaps can hide there. When you find a gap, investigate the cause, fix it, re-test, and record the whole process.
How does the EU AI Act affect AI testing?
The Act's strictest duties apply to high-risk systems and cover risk management, data governance, documentation, oversight, and robustness. A provisional 2026 agreement would move stand-alone high-risk deadlines to December 2027. Testing helps produce evidence for these duties. Confirm current dates and your obligations with qualified legal counsel.