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Explainable AI in Medical Informatics and Decision Support

Call for papers

Based on a successful workshop on explainable AI during the Cross Domain for Machine Learning and Knowledge Extraction (CD-MAKE) 2018 conference, we launch this call for a special issue at BMC Medical Informatics and Decision Making, with the possibility to present the papers at the next session on explainable AI during the CD-MAKE 2019 conference in Kent (Canterbury, UK) at the end of August 2019.

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We want to inspire cross-domain experts interested in artificial intelligence/machine learning to stimulate research, engineering and evaluation in, around and for explainable AI - towards making machine decisions transparent, re-enactive, comprehensible, interpretable, thus explainable, re-traceable and reproducible; the latter is the cornerstone of scientific research per se!

We foster cross-disciplinary and interdisciplinary work including but not limited to:

  • Novel methods, algorithms, tools for supporting explainable AI
  • Proof-of-concepts and demonstrators of how to integrate explainable AI into workflows
  • Frameworks, architectures, algorithms and tools to support post-hoc and ante-hoc explainability and causality machine learning
  • Theoretical approaches of explainability ("What is a good explanation?")
  • Towards argumentation theories of explanation and issues of cognition
  • Comparison Human intelligence vs. Artificial Intelligence (HCI -- KDD)
  • Interactive machine learning with human(s)-in-the-loop (crowd intelligence)
  • Explanation User Interfaces and Human-Computer Interaction (HCI) for explainable AI
  • Novel Intelligent User Interfaces and affective computing approaches
  • Fairness, accountability and trust
  • Ethical aspects, law and social responsibility
  • Business aspects of explainable AI
  • Self-explanatory agents and decision support systems
  • Explanation agents and recommender systems
  • Combination of statistical learning approaches with large knowledge repositories (ontologies)

The grand goal of future explainable AI is to make results understandable and transparent  and to answer questions of how and why a result was achieved. In fact: “Can we explain how and why a specific result was achieved by an algorithm?”

Submission for this special issue is open until 30 March 2019. The special issue is overseen by Section Editor Andreas Holzinger.

For further information, please click here. For pre-submission enquiries, please contact the in-house Editor (Dirk Krüger) at bmcmedinformdecismak@biomedcentral.com.

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