What is AI Bias Documentation?
AI bias documentation is the systematic recording of how bias was evaluated in a dataset or AI system. It captures the evaluation methodology, protected attributes examined, fairness metrics computed, and the limitations of the analysis.
Documentation does not certify the absence of bias — it creates a traceable record that regulators, auditors, and stakeholders can examine. This distinction is legally significant: organizations that document their evaluation process demonstrate due diligence without implying their system is free from risk.
A complete bias evaluation record includes the evaluation method (distributional analysis, fairness metric suite, counterfactual evaluation), the protected attributes assessed, quantitative fairness metrics, class distribution data, known limitations, and the evaluating entity. Each element must be specific enough to allow independent review and reproducibility.
CertifiedData issues bias evaluation records as machine-readable artifacts linked to dataset certificates. These records appear in the public transparency log, making the evaluation history publicly verifiable. The system records what was evaluated, by whom, using which method, and what was found — without asserting conclusions about the acceptability of results.
Organizations deploying AI systems remain responsible for interpreting evaluation results, determining acceptable thresholds, and monitoring for distributional shifts after deployment. Bias documentation is the start of due diligence, not its conclusion.