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CMAI 2020 : 1st Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making

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Link: https://workshop-cmai.github.io/2020/
 
When Nov 3, 2020 - Nov 6, 2020
Where Vienna
Submission Deadline Jul 27, 2020
Notification Due Aug 17, 2020
Final Version Due Sep 7, 2020
Categories    conceptual modelling   artificial intelligence (ai)   data-driven decision making   explainable ai (xai)
 

Call For Papers

The workshop will be held in conjunction with the ER 2020 conference:
https://er2020.big.tuwien.ac.at/

Call for Papers
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Artificial intelligence (AI) is front and center in the data-driven revolution that has been taking place in the last couple of years with the increasing availability of large amounts of data (“big data”) in virtually every domain. The now dominant paradigm of data-driven AI, powered by sophisticated machine learning algorithms, employs big data to build intelligent applications and support fact-based decision making. The focus of data-driven AI is on learning (domain) models and keeping those models up-to-date by using statistical methods over big data, in contrast to the manual modeling approach prevalent in traditional, knowledge-based AI.
While data-driven AI has led to significant breakthroughs, it also comes with a number of disadvantages. First, models generated by machine learning algorithms often cannot be inspected and understood by a human being, thus lacking explainability. Furthermore, integration of preexisting domain knowledge into learned models – prior to or after learning – is difficult. Finally, correct application of data-driven AI depends on the domain, problem, and organizational context while considering human aspects as well. Conceptual modeling can be the key to applying data-driven AI in a meaningful, correct, and time-efficient way while improving maintainability, usability, and explainability.

Topics of Interest
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The topics of interest include, but are not limited to, the following:
- Combining generated and manually engineered models
- Combining symbolic with sub-symbolic models
- Conceptual (meta-)models as background knowledge for model learning
- Explainability of learned models
- Conceptual models for enabling explainability, model validation and plausibility checking
- Trade-off between explainability and model performance
- Trade-off between comprehensibility of an explanation and its completeness
- Reasoning in generated models
- Data-driven modeling support
- Learning of meta-models
- Automatic, incremental model adaptation
- Model-driven guidance and support for data analytics lifecycle
- Conceptual models for supporting users with conducting data analysis

Important Dates
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Paper Submission: 6 July 2020
Author Notification: 27 July 2020
Camera-Ready Paper Submission: 11 August 2020

Submission Guidelines
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Submitted papers must not exceed 10 pages. Accepted papers will be published in the LNCS series by Springer. Note that only accepted papers presented in the workshop by at least one author will be published.

Workshop Organizers
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Dominik Bork, University of Vienna, Austria
Peter Fettke, German Research Center for Artificial Intelligence, Germany
Wolfgang Maass, German Research Center for Artificial Intelligence, Germany
Ulrich Reimer, University of Applied Sciences St. Gallen, Switzerland
Christoph G. Schuetz, Johannes Kepler University Linz, Austria
Marina Tropmann-Frick, University of Applied Sciences Hamburg, Germany
Eric S. K. Yu, University of Toronto, Canada

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