Which of the following controls would BEST reduce the risk of bias being introduced into AI models by flawed training data?
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.
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