CfP Journal (SCI IF=2,5) 2019 : Springer/Nature BMC MIDM Explainable AI in Medical Informatics and Decision Support

Special Collection Springer/Nature BMC Medical Informatics and Decision Support
Full open access SCI-IF = 2,5

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.

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.