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Towards Explainable Artificial Intelligence

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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11700))

Abstract

In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today’s ML algorithms are able to achieve excellent performance (at times even exceeding the human level) on an increasing number of complex tasks. Deep learning models are at the forefront of this development. However, due to their nested non-linear structure, these powerful models have been generally considered “black boxes”, not providing any information about what exactly makes them arrive at their predictions. Since in many applications, e.g., in the medical domain, such lack of transparency may be not acceptable, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This introductory paper presents recent developments and applications in this field and makes a plea for a wider use of explainable learning algorithms in practice.

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Notes

  1. 1.

    The authors of [24] showed that deep models can be easily fooled by physical-world attacks. For instance, by putting specific stickers on a stop sign one can achieve that the stop sign is not recognized by the system anymore.

  2. 2.

    The PASCAL VOC images have been automatically crawled from flickr and especially the horse images were very often copyrighted with a watermark.

  3. 3.

    Traditional methods to evaluate classifier performance require large test datasets.

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Acknowledgements

This work was supported by the German Ministry for Education and Research as Berlin Big Data Centre (01IS14013A), Berlin Center for Machine Learning (01IS18037I) and TraMeExCo (01IS18056A). Partial funding by DFG is acknowledged (EXC 2046/1, project-ID: 390685689). This work was also supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451, No. 2017-0-01779).

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Authors and Affiliations

  1. Fraunhofer Heinrich Hertz Institute, 10587, Berlin, Germany

    Wojciech Samek

  2. Technische Universität Berlin, 10587, Berlin, Germany

    Klaus-Robert Müller

  3. Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea

    Klaus-Robert Müller

  4. Max Planck Institute for Informatics, 66123, Saarbrücken, Germany

    Klaus-Robert Müller

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  1. Fraunhofer Heinrich Hertz Institute, Berlin, Germany

    Wojciech Samek

  2. Technische Universität Berlin, Berlin, Germany

    Grégoire Montavon

  3. University of Oxford, Oxford, UK

    Andrea Vedaldi

  4. Technical University of Denmark, Kgs. Lyngby, Denmark

    Lars Kai Hansen

  5. Sekretariat MAR 4-1, Technical University of Berlin, Berlin, Berlin, Germany

    Klaus-Robert Müller

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Samek, W., Müller, KR. (2019). Towards Explainable Artificial Intelligence. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L., Müller, KR. (eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science(), vol 11700. Springer, Cham. https://doi.org/10.1007/978-3-030-28954-6_1

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