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Some Shades of Grey!: Interpretability and Explainability of Deep Neural Networks

Andreas Dengel
In: Proceedings WCRML19. ACM Workshop on Crossmodal Learning and Application, Ottawa, Canada, ISBN 978-1-4503-6780-6, Association for Computing Machinery, New York, 6/2019.


Based on the availability of data and corresponding computing capacity, more and more cognitive tasks can be transferred to computers, which independently learn to improve our understanding, increase our problem-solving capacity or simply help us to remember connections. Deep neural networks in particular clearly outperform traditional AI methods and thus find more and more areas of application where they are involved in decision-making or even make decisions independently. For many areas, such as autonomous driving or credit allocation, the use of such networks is extremely critical and risky due to their "black box" character, since it is difficult to interpret how or why the models come to certain results. The paper discusses and presents various approaches that attempt to understand and explain decision-making in deep neural networks.

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