Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems
Heinrich K, Zschech P, Skouti T, Griebenow J, Riechert S (2019)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2019
Publisher: Association for Information Systems
Conference Proceedings Title: Proceedings of the 25th Americas Conference on Information Systems
Event location: Cancún
Abstract
The evolution regarding Intelligent Systems Development, especially the advances in the field of Deep Learning Networks facilitate the design of complex prediction systems in multiple fields like image recognition or time series prediction spanning over different sectors like manufacturing or service industry. The accuracy achieved in those complex systems comes at the price of total in non-transparency of model results. In order to tackle that research gap we propose a systematic scheme that proposes several methods for interpreting and visualizing deep neural network results. We then characterize the methods and give a comprehensive overview over the current state of the art and provide limitations and further research proposals.
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Germany (DE)How to cite
APA:
Heinrich, K., Zschech, P., Skouti, T., Griebenow, J., & Riechert, S. (2019). Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems. In Proceedings of the 25th Americas Conference on Information Systems. Cancún, MX: Association for Information Systems.
MLA:
Heinrich, Kai, et al. "Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems." Proceedings of the 25th Americas Conference on Information Systems (AMCIS), Cancún Association for Information Systems, 2019.
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