Model-Agnostic Explanations using Minimal Forcing Subsets

Understand

How can we find a subset of training samples that are most responsible for a specific prediction made by a complex black-box machine learning model? More generally, how can we explain the model's decisions to end-users in a transparent way? We propose a new model-agnostic algorithm to identify a minimal set of training samples that are indispensable for a given model's decision at a particular test point, i.e., the model's decision would have changed upon the removal of this subset from the training dataset.

• Our algorithm identifies such a set of "indispensable" samples iteratively by solving a constrained optimization problem.
• Further, we speed up the algorithm through efficient approximations and provide theoretical justification for its performance.
• To demonstrate the applicability and effectiveness of our approach, we apply it to a variety of tasks including data poisoning detection, training set debugging and understanding loan decisions.