Explainable Argumentation for Wellness Consultation

  • Isabel SassoonEmail author
  • Nadin Kökciyan
  • Elizabeth Sklar
  • Simon Parsons
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11763)


There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers’ intuition of what constitutes a ‘good’ explanation. There exist vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations to the explanation process. This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.


Explanation Explainability Interpretability Explainable AI Transparency 



This work was funded by EPSRC grant EP/P010105/1 CONSULT: Collaborative Mobile Decision Support for Managing Multiple Morbidities.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Isabel Sassoon
    • 1
    Email author
  • Nadin Kökciyan
    • 1
  • Elizabeth Sklar
    • 1
  • Simon Parsons
    • 1
  1. 1.Department of InformaticsKing’s College LondonLondonUK

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