XI-ML 2020
Explainable and Interpretable Machine Learning 2020

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