"Why Should I Trust You?": Explaining the Predictions of Any Classifier (LIME)

Invented Local Interpretable Model-agnostic Explanations (LIME), approximating complex black-box decision boundaries locally using sparse linear surrogate models.

Serves as qualified reference for implementing task-model-explainability in production systems.

Assumptions

• Underlying computational topology and mathematical bounds adhere to established convexity/smoothness guarantees

Limitations

• Hardware runtime speedups, privacy budgets, and convergence depend on hyperparameters and network communication limits