  Explainability algorithms aimed at interpreting decision-making AI systems
usually consider balancing two critical dimensions: 1) \textit{faithfulness},
where explanations accurately reflect the model's inference process. 2)
\textit{plausibility}, where explanations are consistent with domain experts.
However, the question arises: do faithfulness and plausibility inherently
conflict? In this study, through a comprehensive quantitative comparison
between the explanations from the selected explainability methods and
expert-level interpretations across three NLP tasks: sentiment analysis, intent
detection, and topic labeling, we demonstrate that traditional
perturbation-based methods Shapley value and LIME could attain greater
faithfulness and plausibility. Our findings suggest that rather than optimizing
for one dimension at the expense of the other, we could seek to optimize
explainability algorithms with dual objectives to achieve high levels of
accuracy and user accessibility in their explanations.
