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.

Accepted Papers

• How to Induce Trust in Medical AI Systems
  Ulrich Reimer, Beat Tödtli and Edith Maier Preprint Paper link
• Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter
  Pulkit Sharma, Shezan Mirzan, Apurva Bhandari, Anish Pimpley, Abhiram Eswaran, Soundar Srinivasan and Liqun Shao Preprint Paper link
• Towards Automated Support for Conceptual Model Diagnosis and Repair
  Mattia Fumagalli, Tiago Prince Sales and Giancarlo Guizzardi Preprint Paper link
• Superimposition: Augmenting Machine Learning Outputs with Conceptual Models for Explainable AI
  Roman Lukyanenko, Arturo Castellanos, Veda Storey, Alfred Castillo, Monica Chiarini Tremblay and Jeffrey Parsons Preprint Paper link

Topics of Interest

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

Paper submission: 6 July 2020 27 July 2020 (Extension due to COVID-19 situation)

Author notification: 27 July 2020 17 August 21 August 2020

Camera-ready Version: 11 August 2020 7 September 2020

Paper Submission

Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made.

Papers must not contain and author information (i.e., blind submission) and must not exceed 10 pages (including figures, references, etc.) in length using the LNCS template. Submissions are handled in the EasyChair system. Click here to submit your paper.

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

• Dominik Bork, TU Wien, 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

Program Committee

• Klaus-Dieter Althoff, DFKI / University of Hildesheim, Germany
• Kerstin Bach, Norwegian University of Science and Technology, Norway
• Ralph Bergmann, University of Trier, Germany
• Loris Bozzato, Fondazione Bruno Kessler, Italy
• Isabelle Comyn-Wattiau, ESSEC & CNAM, France
• Ernesto Damiani, University of Milan, Italy
• Tatiana Endrjukaite, NTT
• Michael Fellmann, University of Rostock, Germany
• Hans-Georg Fill, University of Fribourg, Switzerland
• Aditya Ghose, University of Wollongong, Australia
• Knut Hinkelmann, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Switzerland
• Kamalakar Karlapalem, IIIT Hyderabad, India
• Josef Küng, Johannes Kepler University Linz, Austria
• Julio Cesar Leite, PUC-Rio, Brasil
• Bernd Neumayr, Johannes Kepler University Linz, Austria
• Jeffrey Parsons, University of Newfoundland, Canada
• Barbara Re, University of Camerino, Italy
• Oscar Romero, Universitat Politècnica de Catalunya, Spain
• Matt Selway, University of South Australia, Adelaide
• Bernhard Thalheim, Christian Albrechts University Kiel, Germany
• Stefan Thalmann, University of Graz, Austria
• Rosina Weber, Drexel University, USA
• Tatjana Welzer, University of Maribor, Slovenia
• Mathias Weske, University of Potsdam, Germany
• Nil Wick, epworth
• Takahira Yamaguchi, Keio University, Japan