AI Expert Audit
More organizations now use AI systems to produce analysis that informs real decisions: evaluations, forecasts, cost estimates, policy and investment briefs. The question a board, regulator or funder will ask is simple. Can you defend it? An AI Expert Audit tests whether your system performs at the level of the experts it is meant to support, where it fails, and what would fix it.
Questions this answers
- Does our AI system produce analysis as good as a qualified expert would, on the tasks we actually use it for?
- Does it beat a simpler, cheaper approach, or only look more impressive?
- Are its numbers calculated, its sources real and its causal claims justified?
- Would it give the same answer if we ran it again, or if a reasonable assumption changed?
- Where do people need to stay in control, and is that control real?
Methods
Comparison with expert and simpler baselines on cases with known answers, removal tests that show which parts of the system earn their place, checks of causal and statistical reasoning, source and number tracing, repeat runs and alternative specifications, and adversarial cases designed to provoke failure. Scored against the Evidence-Gated AI Checklist.
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