Interpretation Quality Score for Measuring the Quality of interpretability methods

Understand

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.