On Validating, Repairing and Refining Heuristic ML Explanations
This work addresses the reliability of explanations for non-interpretable ML models, highlighting a critical gap in current heuristic methods.
The paper assessed the quality of heuristic explanations for machine learning models, particularly boosted trees, and found that most heuristic explanations were inadequate when considering the entire instance space.
Recent years have witnessed a fast-growing interest in computing explanations for Machine Learning (ML) models predictions. For non-interpretable ML models, the most commonly used approaches for computing explanations are heuristic in nature. In contrast, recent work proposed rigorous approaches for computing explanations, which hold for a given ML model and prediction over the entire instance space. This paper extends earlier work to the case of boosted trees and assesses the quality of explanations obtained with state-of-the-art heuristic approaches. On most of the datasets considered, and for the vast majority of instances, the explanations obtained with heuristic approaches are shown to be inadequate when the entire instance space is (implicitly) considered.