Abstract
Despite its popularity, the decision making process of a Deep Neural Network (DNN) model is opaque to users, making it difficult to understand the behaviour of the model. We present the design of a Web-based DNN interpretability framework which is based on the core notions in case-based reasoning approaches where exemplars (e.g., data points considered similar to a chosen data point) are utilised to help achieve effective interpretation. We demonstrate the framework via a Web based tool called Deep Explorer (DeX) and present the results of user acceptance studies. Our studies showed the effectiveness of the tool in gaining a better understanding of the decision making process of a DNN model as well as the efficacy of the case-based approach in improving DNN interpretability.
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Notes
- 1.
A video presentation of the system is available from: https://youtu.be/E87X9U53sXg.
- 2.
We used MNIST [10] in our case study implementation.
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The authors thank all participants who took part in the application user study.
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Nadeem, R., Wu, H., Paik, Hy., Wang, C. (2019). A Case Based Deep Neural Network Interpretability Framework and Its User Study. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham. https://doi.org/10.1007/978-3-030-34223-4_10
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