AI Audit
AI GovernanceWhat It Means
An AI audit is a comprehensive review of your AI systems to verify they're working as intended and meeting all legal, ethical, and company requirements. It's like a financial audit, but specifically examining how your AI makes decisions, what data it uses, and whether it's producing fair and compliant outcomes.
Why Chief AI Officers Care
AI audits help you proactively identify risks before they become costly regulatory violations, discrimination lawsuits, or reputation damage. They provide the documentation and evidence needed to demonstrate responsible AI governance to regulators, customers, and stakeholders, while ensuring your AI systems align with business objectives and values.
Real-World Example
A bank's AI lending system undergoes an audit that examines the training data for bias, tests decision outcomes across different demographic groups, reviews model documentation, and verifies compliance with fair lending laws - ultimately revealing that the system unfairly denies loans to certain zip codes, prompting immediate corrective action.
Common Confusion
Many assume AI audits are only needed when problems arise, but they're actually preventive measures that should happen regularly throughout an AI system's lifecycle. It's also common to think technical testing alone constitutes an audit, when comprehensive audits must also examine business processes, governance, and real-world impacts.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, AI audits are critical for ensuring diagnostic algorithms, treatment recommendation systems, and clinical...
Finance: In finance, AI audits are critical for ensuring algorithmic trading systems, credit scoring models, and fraud detection ...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
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