Interpretation Quality Score for Measuring the Quality of interpretability methods
This addresses a fundamental gap for researchers and practitioners in ML and NLP by enabling comparison and ranking of interpretability methods, though it is incremental as it builds on existing methods.
The paper tackles the lack of a standard metric to evaluate the quality of explanations from interpretability methods in machine learning, proposing a novel metric and testing it on three NLP tasks with six methods.
Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years. In addition to making accurate decisions, the necessity of understanding how models make their decisions has become apparent in many applications. To that end, many interpretability methods that help explain the decision processes of ML models have been developed. Yet, there currently exists no widely-accepted metric to evaluate the quality of explanations generated by these methods. As a result, there currently is no standard way of measuring to what degree an interpretability method achieves an intended objective. Moreover, there is no accepted standard of performance by which we can compare and rank the current existing interpretability methods. In this paper, we propose a novel metric for quantifying the quality of explanations generated by interpretability methods. We compute the metric on three NLP tasks using six interpretability methods and present our results.