Bias Audit
An evaluation process to detect and mitigate biases in AI systems, ensuring fairness and compliance with ethical standards.
A structured review - often by an independent team or external firm - that examines every stage of the AI lifecycle (data collection, preprocessing, modeling, evaluation) for bias. Auditors apply statistical tests (e.g., disparate impact), model explainability tools, and user-group analyses. The process ends with concrete remediation recommendations and governance updates.
A bank commissions a bias audit of its credit-scoring AI. Auditors sample loan decisions by demographic group, find that applicants from certain ZIP codes are disproportionately denied, and recommend data-augmentation and new fairness constraints in the scoring algorithm, after which the bank monitors denial rates monthly to track progress.
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Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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