Debugging Tests for Model Explanations (2011.05429v1)

Abstract: 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.

• The paper's primary contribution is the categorization of model errors into data, model, and test-time contamination, forming a basis for effective debugging.
• The study evaluates methods like Integrated Gradients and Guided Backpropagation, showing they can detect spurious correlations but often miss mislabeled training examples.
• A human subject study reveals that end-users favor model predictions over attributions, underscoring the challenges of applying these methods in practical debugging.

Analyzing the Efficacy of Feature Attribution Methods for Model Debugging

The paper "Debugging Tests for Model Explanations" by Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim, provides a thorough investigation into the effectiveness of feature attribution methods in identifying and diagnosing errors in machine learning models. This work is situated in the context of an increasing need for explainable AI, especially in high-stakes settings like healthcare where model errors can lead to significant consequences.

Overview of Research Contributions

The paper makes several key contributions:

1. Bug Categorization: The research categorizes model errors into three main types: data contamination, model contamination, and test-time contamination. This categorization forms the basis for assessing the utility of feature attribution methods in debugging these specific types of errors.
2. Empirical Evaluation: A comprehensive empirical assessment is conducted to evaluate several feature attribution methods, including Gradient, Integrated Gradients, and SmoothGrad, among others. These methods are tested against specific bugs, such as spurious correlation artifacts and mislabeled examples.
3. Insights and Findings: The paper finds that while certain methods are capable of diagnosing spurious correlation artifacts, they fall short in identifying mislabeled training examples. Moreover, methods that modify backpropagation, such as Guided Backpropagation and Layer-Wise Relevance Propagation, show invariance to model parameter changes, thus limiting their effectiveness in detecting model contamination bugs.
4. Human Subject Study: A user paper with human participants reveals that end-users predominantly rely on model predictions rather than attributions to identify defective models, highlighting a gap between the availability of explanation methods and their practical utility in debugging.

Implications and Future Directions

The paper's findings have both practical and theoretical implications. Practically, they suggest that practitioners need to be cautious in relying solely on feature attribution methods for model debugging. Theoretically, the research invites further exploration into developing more robust and reliable explanation methods that can effectively assist in diagnosing a broader range of model errors.

Future work could delve into integrating other explanation methods, such as concept activation vectors or influence functions, which were briefly explored and showed potential in diagnosing specific bugs. Additionally, expanding the bug categorization to encompass more complex real-world scenarios could provide a more holistic understanding of model errors and the tools needed to debug them.

Conclusion

This paper systematically assesses the practicality of feature attribution methods for model debugging. While contributing valuable insights into the limitations and potential of current methods, it also lays the groundwork for future research aimed at enhancing the reliability and scope of model explanations in debugging tasks. The significance of this research becomes apparent when considering the increasing deployment of machine learning models in critical operational settings, where understanding and correcting model behavior is paramount.