Algorithmic Audit Explainer
An algorithmic audit is a systematic examination of an automated system against defined criteria. The audit may evaluate technical performance, fairness, privacy, security, documentation, governance, or compliance, depending on its purpose and scope.
Auditors gather evidence from records, interviews, data, code, model outputs, and control tests. Some audits are internal and support development; others are independent and provide assurance to regulators, customers, workers, or the public.
Meaningful audits require enough access to test relevant claims. Black-box query access may reveal behavior, while deeper access can help trace causes. Trade secrets and privacy constraints must be managed without making verification impossible.
An audit is not a universal certificate of safety. Findings are limited by the chosen criteria, evidence, samples, timing, and system version. Recommendations need owners and follow-up so discovered gaps lead to corrective action.
An algorithmic audit systematically evaluates an automated system, its development process, and its outcomes against defined criteria and evidence.
Key terms include audit, criterion, evidence, scope, independence, black-box access, control test, finding and remediation.
Auditors need explicit standards, claims, or requirements against which to evaluate evidence.
A narrow scope cannot support broad conclusions about every possible harm.
Behavioral testing, documents, data, and implementation details reveal different kinds of evidence.
The audit plan should justify which access is necessary for the questions being asked.
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