Fetching the paper…

Reading the bibliography…

2019

Analyzing the Interpretability Robustness of Self-Explaining Models

Zheng, Haizhong, Fernandes, Earlence, Prakash, Atul

Original
PDF
Post on X

Understand

Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness.

We evaluate the interpretability robustness of SEMs and show that explanations provided by SEMs as currently proposed are not robust to adversarial inputs.
Specifically, we successfully created adversarial inputs that do not change the model outputs but cause significant changes in the explanations.
We find that even though current SEMs use stable co-efficients for mapping explanations to output labels, they do not consider the robustness of the first stage of the model that creates interpretable basis concepts from the input, leading to non-robust explanations.

Analyzing the Interpretability Robustness of Self-Explaining Models · Around

Built on

Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
Earlier work this paper cites.
Layer-wise relevance propagation for neural networks with local renormalization layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., and Samek, W · 2016
Earlier work this paper cites.
Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Earlier work this paper cites.

Similar

Axiomatic attribution for deep networks
Original
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
This looks like that: deep learning for interpretable image recognition
Original
Chen, C., Li, O., Barnett, A., Su, J., and Rudin, C · 2018
Cited alongside, same era.
Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
Li, O., Liu, H., Chen, C., and Rudin, C · 2018
Cited alongside, same era.
On the robustness of interpretability methods
Alvarez-Melis, D. and Jaakkola, T. S
Cited in the paper.
Towards robust interpretability with self-explaining neural networks
Alvarez-Melis, D. A. and Jaakkola, T
Cited in the paper.

Then

Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Later among the works it cites.
Interpretation of neural networks is fragile
Ghorbani, A., Abid, A., and Zou, J · 2019
Closest in time.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…