AAIA: Advanced in AI Audit

Cross-validation evaluates the model across multiple training and validation partitions, making it less likely that a single unrepresentative or flawed data split will determine the model assessment. It can reveal inconsistent performance and bias-related limitations that require remediation. NIST identifies cross-validation as a standard validation approach and calls for evaluating fairness and bias in AI systems.