Evaluating Attribution Methods using White-Box LSTMs

Yiding Hao


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.
Anthology ID:
2020.blackboxnlp-1.28
Volume:
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Month:
November
Year:
2020
Address:
Online
Venue:
BlackboxNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
300–313
Language:
URL:
https://aclanthology.org/2020.blackboxnlp-1.28
DOI:
10.18653/v1/2020.blackboxnlp-1.28
Bibkey:
Cite (ACL):
Yiding Hao. 2020. Evaluating Attribution Methods using White-Box LSTMs. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 300–313, Online. Association for Computational Linguistics.
Cite (Informal):
Evaluating Attribution Methods using White-Box LSTMs (Hao, BlackboxNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.blackboxnlp-1.28.pdf
Video:
 https://slideslive.com/38939768
Code
 yidinghao/whitebox-lstm