Timezone: »

 
Poster
Debugging Tests for Model Explanations
Julius Adebayo · Michael Muelly · Ilaria Liccardi · Been Kim

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1826
in Poster Session 7 »

We investigate whether post-hoc model explanations are effective for diagnosing model errors--model debugging. In response to the challenge of explaining a model's prediction, a vast array of explanation methods have been proposed. Despite increasing use, it is unclear if they are effective. To start, we categorize \textit{bugs}, based on their source, into: ~\textit{data, model, and test-time} contamination bugs. For several explanation methods, we assess their ability to: detect spurious correlation artifacts (data contamination), diagnose mislabeled training examples (data contamination), differentiate between a (partially) re-initialized model and a trained one (model contamination), and detect out-of-distribution inputs (test-time contamination). We find that the methods tested are able to diagnose a spurious background bug, but not conclusively identify mislabeled training examples. In addition, a class of methods, that modify the back-propagation algorithm are invariant to the higher layer parameters of a deep network; hence, ineffective for diagnosing model contamination. We complement our analysis with a human subject study, and find that subjects fail to identify defective models using attributions, but instead rely, primarily, on model predictions. Taken together, our results provide guidance for practitioners and researchers turning to explanations as tools for model debugging.

Author Information

Julius Adebayo (MIT)
Michael Muelly (Stanford University)
Ilaria Liccardi (MIT)
Been Kim (Google)

More from the Same Authors