In Depth
An AI audit is a structured assessment of an AI system that examines its technical performance, potential biases, safety properties, compliance with regulations, and alignment with ethical standards. Audits can be internal (conducted by the organization) or external (performed by independent third parties), and they may be voluntary or mandated by regulations like the EU AI Act.
AI audits typically examine several dimensions: accuracy and performance metrics across different user groups (detecting disparate impact), training data composition and quality, model documentation completeness, security vulnerabilities, privacy compliance, and alignment with the system's stated purpose. Specialized tools and frameworks like IBM's AI Fairness 360, Google's What-If Tool, and Microsoft's Responsible AI Dashboard support the auditing process.
The practice of AI auditing is rapidly maturing as regulations increasingly require it. New York City's Local Law 144 mandates bias audits for AI in hiring decisions. The EU AI Act requires conformity assessments for high-risk AI systems. Professional AI auditing firms are emerging, and standards bodies are developing certification criteria. For businesses deploying AI in consequential decisions (hiring, lending, healthcare), regular AI audits are becoming essential risk management practice.