COMPEL Glossary / bias-auditing
Bias Auditing
Bias auditing is the systematic review of AI training data and model outputs to identify and measure unfair biases.
What this means in practice
For training data, auditing examines underrepresentation (are certain groups absent or underrepresented?), historical biases (does the data reflect past discriminatory practices?), and proxy variables (do seemingly neutral features like zip code correlate with protected characteristics?). For model outputs, auditing applies statistical measures like demographic parity, equalized odds, and calibration across relevant demographic groups. Bias auditing is not a one-time activity -- it must be conducted before deployment and continuously in production, because bias can emerge over time as data distributions shift. In the COMPEL framework, bias auditing is a mandatory component of the Evaluate stage and a non-negotiable gate criterion for production deployment.
Why it matters
Bias auditing is not a one-time activity — bias can emerge over time as data distributions shift, making continuous auditing essential. Organizations that audit only before deployment risk releasing AI systems that become discriminatory in production. Regulatory frameworks increasingly require documented bias auditing as evidence of responsible AI practices, making this both an ethical imperative and a compliance necessity.
How COMPEL uses it
Bias auditing is a mandatory component of the Evaluate stage and a non-negotiable gate criterion for production deployment within the Governance pillar. During Model, auditing methodologies and fairness metrics are selected based on the application context. The Produce stage implements automated bias monitoring for production systems. The Learn stage reviews audit findings across the portfolio to identify systemic bias patterns that require upstream corrections to data or model design.
Related Terms
Other glossary terms mentioned in this entry's definition and context.