Which practice involves reviewing and auditing an AI model's decision-making process to ensure its outputs can be explained and understood by security practitioners?

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A. Model explainability and interpretability. Model explainability and interpretability are practices that make AI decisions understandable to humans — critical in security contexts where analysts must validate AI findings before acting. The SecAI+ objectives include securing AI systems and ensuring practitioners can evaluate AI output quality. Model quantization reduces model size for efficiency, not transparency. Hyperparameter tuning optimizes model performance, not decision transparency.

Which practice involves reviewing and auditing an AI model's decision-making process to ensure its outputs can be explained and understood by security practitioners?

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