How AI Models Work

An AI system does not meet a rule merely because a reviewer reads ten answers and finds nothing alarming. Engineers need layered tests that examine ordinary behaviour, difficult cases, safeguards, logs, permissions and human review.

An AI audit cannot rely on someone saying, “We tested the system and it looked fine.” Auditors need evidence showing what the system was designed to do, which version was used, how it was tested and what happened after deployment.

An AI bias audit is not a conversation in which one evaluator asks a chatbot a few questions and decides whether it seems fair. A meaningful audit uses defined test sets, subgroup measurements, error analysis, controlled comparisons, human review, documentation, and monitoring of the wider system.