Faithful to Whom? Questioning Interpretability Measures in NLP
Evan Crothers Herna Viktor Nathalie Japkowicz
A common approach to quantifying model interpretability is to calculate
faithfulness metrics based on iteratively masking input tokens and measuring
how much the predicted label changes as a result. However, we show that such
metrics are generally not suitable for comparing the interpretability of
different neural text classifiers as the response to masked inputs is highly
model-specific. We demonstrate that iterative masking can produce large
variation in faithfulness scores between comparable models, and show that
masked samples are frequently outside the distribution seen during training. We
further investigate the impact of adversarial attacks and adversarial training
on faithfulness scores, and demonstrate the relevance of faithfulness measures
for analyzing feature salience in text adversarial attacks. Our findings
provide new insights into the limitations of current faithfulness metrics and
key considerations to utilize them appropriately.