A Comparative Study of Faithfulness Metrics for Model Interpretability Methods

TL;DR

This paper compares various faithfulness metrics for model interpretability, revealing conflicting preferences among them and evaluating their diagnosticity and computational efficiency.

Contribution

It provides a comprehensive comparison of faithfulness metrics, introducing assessment dimensions and highlighting the superior diagnosticity and efficiency of sufficiency and comprehensiveness.

Findings

Sufficiency and comprehensiveness metrics have higher diagnosticity.

These metrics also exhibit lower time complexity.

Conflicting preferences are observed among different faithfulness metrics.

Abstract

Interpretation methods to reveal the internal reasoning processes behind machine learning models have attracted increasing attention in recent years. To quantify the extent to which the identified interpretations truly reflect the intrinsic decision-making mechanisms, various faithfulness evaluation metrics have been proposed. However, we find that different faithfulness metrics show conflicting preferences when comparing different interpretations. Motivated by this observation, we aim to conduct a comprehensive and comparative study of the widely adopted faithfulness metrics. In particular, we introduce two assessment dimensions, namely diagnosticity and time complexity. Diagnosticity refers to the degree to which the faithfulness metric favours relatively faithful interpretations over randomly generated ones, and time complexity is measured by the average number of model forward passes. According to the experimental results, we find that sufficiency and comprehensiveness metrics have higher diagnosticity and lower time complexity than the other faithfulness metric