This page is current as of 23.03.2020 12:00 MST
Call for Papers – Explainable AI
in Medical Informatics & Decision Making (due to December, 31, 2020)
For details please refer to the workshop papge: https://human-centered.ai/explainable-ai-2020/
For any inquiries please contact Andreas Holzinger, section editor of Springer/Nature BMC MIDM directly.
With this special collection 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, and it is of utmost importance for decision support.
INSTRUCTIONS FOR REVIEWERS:
Each paper will be assigned a minimum of two reviewers to ensure the highest possible quality. Reviewers are asked to provide detailed and constructive comments that will help not only the editors on decision making, but also to help the authors to improve their manuscript aiming at bringing a clear benefit to the readers of the journal. Reviewers are encouraged to provide references to substantiate their comments.
Here are the instructions for reviewers > Guide for BMC Bioinformatics reviewers
INSTRUCTIONS FOR AUTHORS:
All submissions must follow the > Instructions for authors – BMC Appendix B. We encourage authors to submit original research following the guidelines and requirements of BMC Bioinformatics. All articles submitted to this special issue must be based on original research, following the guidelines and requirements of BMC https://www.biomedcentral.com/about/duplicatepublication
Authors are encouraged to use LaTeX, an Template is available here:
We focus on an overall acceptance rate of approximately 20 % targeting for around 8 papers, plus 1 Editorial and 1 Tutorial, so approximately 10 Papers. However, we will focus on quality instead of quantity, so it may be less or more, dependent on the quality of the submissions received.