| Vol-2796 urn:nbn:de:0074-2796-3 Copyright © 2020 for the individual papers by the papers' authors. Copyright © 2020 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# XI-ML 2020
Explainable and Interpretable Machine Learning 2020

### Proceedings of the First International Workshop on Explainable and Interpretable Machine Learning (XI-ML 2020)
co-located with the 43rd German Conference on Artificial Intelligence (KI 2020)

### Bamberg, Germany, September 21, 2020 (Virtual Workshop).

Edited by

### Martin Atzmueller * ***
Tomas Kliegr **
Ute Schmid ***

* Osnabrück University, Germany
** University of Economics Prague, Czech Republic
*** University of Bamberg, Germany

## Table of Contents

- Preface - Proceedings of the 1st International Workshop on Explainable and Interpretable Machine Learning (XI-ML)
  Martin Atzmueller, Tomas Kliegr, Ute Schmid
  Summary: There were 8 papers submitted for peer-review to this workshop.
  Out of these, 7 papers were accepted for this volume,
  5 as regular papers and 2 as short papers.

### Invited talk

- How to Teach a Computer to Learn About Microbes: KG-COVID-19 and Microbial Graph Learning - Abstract
  Marcin P. Joachimiak

### Main Session

- Explaining Multivariate Time Series Forecasts: An Application to Predicting the Swedish GDP
  Henrik Boström, Peter Höglund, Sven-Olof Junker, Ann-Sofie Öberg, Martin Sparr
- Towards Participatory Design Spaces for Explainable AI Interfaces in Expert Domains (Short Paper)
  Henrik Mucha, Sebastian Robert, Rüdiger Breitschwerdt, Michael Fellmann
- Teaching AI to Explain its Decisions Can Affect Class Balance
  Kevin Volkert
- Explainability and the Intention to Use AI-based Conversational Agents (Short Paper)
  Jürgen Fleiß, Elisabeth Bäck, Stefan Thalmann
- Foundations for Solving Classification Problems with Quantitative Abstract Argumentation
  Nico Potyka
- Sequential Exceptional Pattern Discovery Using Pattern-Growth: An Extensible Framework for Interpretable Machine Learning on Sequential Data
  Dennis Mollenhauer, Martin Atzmueller
- A Comparative Study of Explainer Modules Applied to Automated Skin Lesion Classification
  Jia Sun, Tapabrata Chakraborti, J. Alison Noble

2020-12-31: submitted by Martin Atzmueller, metadata incl. bibliographic data published under Creative Commons CC0
2021-01-05: published on CEUR-WS.org |valid HTML5|