posted by organizer: Aholzinger || 1718 views || tracked by 5 users: [display]

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

FacebookTwitterLinkedInGoogle

Link: https://hci-kdd.org/special-issue-explainable-ai-medical-informatics-decision-making/
 
When N/A
Where N/A
Submission Deadline Mar 30, 2019
Categories    explainability   explainable ai   causality   transparent machine learning
 

Call For Papers

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.


Related Resources

ReVIEWING BMC 2021   CFP: 12th Annual ReVIEWING Black Mountain College International Conference
XSA 2021   Explainable Deep Learning for Sentiment Analysis
Bio-inspired Deep Learning 2021   CFP: Bio-inspired Deep Learning Image and Signal Processing Pipelines in Medical Oncology - PeerJ
XKDD 2021   3rd International Workshop on eXplainable Knowledge Discovery in Data Mining
COLA Journal (Elsevier) 2021   CFP: Special issue on “Methods, Tools and Languages for Model-driven Engineering and Low-code Development”
AIMLAI 2021   4th International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
POLTRANCOV 2021   CfP: Policy Transfer and Learning in the Context of COVID-19 Pandemic
NMR 2021   19th International Workshop on Non-Monotonic Reasoning
SI-D2CSCAI 2021   CfP - Special Issue Data-Driven Cybersecurity and Safety for Critical Applications and Infrastructures (MDPI Sensors - IF= 3.275)
MdE Cfp 2021   CFP The political is symbolic - Materiali di Estetica 8.2