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Ambient Explanations: Ambient Intelligence and Explainable AI

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Ambient Intelligence (AmI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11912))

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Abstract

With renewed prominence of Explainable AI (XAI), many areas are revisiting early work on Explainability. Within the broader field of Artificial Intelligence (AI), Ambient Intelligence (AmI) has an advantage in the development of transparent and ethical systems because such work has long been an integral part of research, development and operations in AmI. In this paper we argue that, because of the paradigm requirements of system intelligence, social intelligence and embeddedness, AmI is uniquely prepared to support the push for ethical and transparent technology development. We argue that Ambient Intelligent Systems are well suited to infer when an explanation might be needed (and of what kind), and the form that it should take. We further propose AmI devices as mediators between humans and machines because they can combine social and technical systems in a fully embedded way.

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Author information

Authors and Affiliations

  1. University of Hildesheim, 31141, Hildesheim, Germany

    Jörg Cassens

  2. Paris Lodron University of Salzburg, 5020, Salzburg, Austria

    Rebekah Wegener

Authors
  1. Jörg Cassens
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  2. Rebekah Wegener
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Correspondence to Jörg Cassens .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Ioannis Chatzigiannakis

  2. Philips Research, Eindhoven, The Netherlands

    Boris De Ruyter

  3. Hellenic Open University, Patras, Greece

    Irene Mavrommati

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Cassens, J., Wegener, R. (2019). Ambient Explanations: Ambient Intelligence and Explainable AI. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_30

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  • DOI: https://doi.org/10.1007/978-3-030-34255-5_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34254-8

  • Online ISBN: 978-3-030-34255-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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