An ML practitioner wants to offer stakeholders transparency and explanations of how a model makes predictions. Which of the following best provides that explainability?

Shapley values provide a robust method for attributing the contribution of each feature to a model's prediction, offering detailed, game-theoretic explanations that enhance transparency for stakeholders. This allows practitioners to explain why a model made a specific prediction. Model accuracy measures the overall correctness but doesn't explain individual predictions. A confusion matrix details classification performance (true positives, false positives, etc.) but also lacks individual prediction explainability. A secure model inference endpoint is about deployment and security, not model explainability.