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arXiv · 2010.08606

Evaluating Attribution Methods using White-Box LSTMs

Yiding Hao
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Abstract

Interpretability methods for neural networks are difficult to evaluate because we do not understand the black-box models typically used to test them. This paper proposes a framework in which interpretability methods are evaluated using manually constructed networks, which we call white-box networks, whose behavior is understood a priori. We evaluate five methods for producing attribution heatmaps by applying them to white-box LSTM classifiers for tasks based on formal languages. Although our white-box classifiers solve their tasks perfectly and transparently, we find that all five attribution methods fail to produce the expected model explanations.

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Yiding Hao. 2020-10-16. Evaluating Attribution Methods using White-Box LSTMs. https://arxiv.org/abs/2010.08606

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