AI Bias Audit Trail
An AI bias audit trail is a chronological, tamper-evident record of every bias evaluation performed on a dataset or AI system. It enables regulators, auditors, and stakeholders to verify that evaluation procedures were conducted and what was found.
An audit trail differs from a single evaluation record in scope: it captures the history of evaluations across the AI lifecycle — initial dataset evaluation, re-evaluation after data updates, post-deployment monitoring, and third-party review records. The audit trail must be immutable: entries can be added but not modified.
CertifiedData maintains bias evaluation records in a public transparency log. Each entry is cryptographically linked to the dataset certificate and recorded with a timestamp and issuer signature. This creates a tamper-evident audit trail that organizations can reference in regulatory documentation without relying on self-attestation alone.
Under the EU AI Act, high-risk AI system providers must maintain records that enable reconstruction of the system's development history — including training data governance and bias examinations. An audit trail that links dataset certificates to bias evaluation records to deployment timestamps supports this reconstruction.
NIST AI RMF's Govern and Manage functions both require ongoing documentation and monitoring. An audit trail satisfies the Manage function's expectation that bias risk is monitored over time, not only at initial deployment.