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A Social Science-based Approach to Explanations for (Game) AI

Vanessa Volz,K. Majchrzak,M. Preuss

Published 2018 in IEEE Conference on Computational Intelligence and Games

ABSTRACT

The current AI revolution provides us with many new, but often very complex algorithmic systems. This complexity does not only limit understanding, but also acceptance of e.g. deep learning methods. In recent years, explainable AI (XAI) has been proposed as a remedy. However, this research is rarely supported by publications on explanations from social sciences. We suggest a bottom-up approach to explanations for (game) AI, by starting from a baseline definition of understandability informed by the concept of limited human working memory. We detail our approach and demonstrate its application to two games from the GVGAI framework. Finally, we discuss our vision of how additional concepts from social sciences can be integrated into our proposed approach and how the results can be generalised.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    IEEE Conference on Computational Intelligence and Games

  • Publication date

    2018-08-01

  • Fields of study

    Sociology, Computer Science, Psychology

  • Identifiers
    DOI 10.1109/CIG.2018.8490361
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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