Bias Auditing Methodology

Audit an ML model for bias rigorously. Learn the audit lifecycle (scope, data, metrics, tests, findings, mitigations, follow-up), scoping difficult cases, data acquisition for sensitive attributes, metric computation across slices, statistical testing, root-cause analysis, and the auditor-facing report regulators read.