Due to the black-box nature of deep learning models, methods for explaining the models’ results are crucial to gain trust from humans and support collaboration between AIs
and humans. In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2)
justifying model predictions, and (3) helping humans investigate uncertain predictions.
The results highlight dissimilar qualities of the
various explanation methods we consider and
show the degree to which these methods could
serve for each purpose.