FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions

Authors

  • Sandareka Wickramanayake National University of Singapore
  • Wynne Hsu National University of Singapore
  • Mong Li Lee National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v33i01.33012539

Abstract

Explaining the decisions of a Deep Learning Network is imperative to safeguard end-user trust. Such explanations must be intuitive, descriptive, and faithfully explain why a model makes its decisions. In this work, we propose a framework called FLEX (Faithful Linguistic EXplanations) that generates post-hoc linguistic justifications to rationalize the decision of a Convolutional Neural Network. FLEX explains a model’s decision in terms of features that are responsible for the decision. We derive a novel way to associate such features to words, and introduce a new decision-relevance metric that measures the faithfulness of an explanation to a model’s reasoning. Experiment results on two benchmark datasets demonstrate that the proposed framework can generate discriminative and faithful explanations compared to state-of-the-art explanation generators. We also show how FLEX can generate explanations for images of unseen classes as well as automatically annotate objects in images.

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Published

2019-07-17

How to Cite

Wickramanayake, S., Hsu, W., & Lee, M. L. (2019). FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2539-2546. https://doi.org/10.1609/aaai.v33i01.33012539

Issue

Section

AAAI Technical Track: Human-AI Collaboration