| Vol-3208 urn:nbn:de:0074-3208-1 Copyright © 2022 for the individual papers by the papers' authors. Copyright © 2022 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).

# ArgML 2022
Argumentation & Machine Learning

### Proceedings of the 1st Workshop on Argumentation & Machine Learning
co-located with 9th International Conference on Computational Models of Argument (COMMA 2022)

### Cardiff, Wales, September 13th, 2022.

Edited by

### Isabelle Kuhlmann(1)
Jack Mumford(2)
Stefan Sarkadi(3)

(1) Artificial Intelligence Group, University of Hagen, Germany
(2) Department of Computer Science, University of Liverpool, UK
(3) Department of Informatics, King's College London, UK

## Table of Contents

Summary: There were 9 papers submitted for peer-review to this workshop. Out of these, 7 papers were accepted for this volume.
Programme Committee

- Argumentation-based Explainable Machine Learning (ArgEML): a Real-life Use Case on Gynecological Cancer 1-13
  Nicoletta Prentzas, Athena Gavrielidou, Marios Neofytou, Antonis Kakas
- Everyday Argumentative Explanations for Classification 14-26
  Jowan van Lente, Annemarie Borg, Floris Bex
- Optimizing Language Models for Argumentative Reasoning 27-44
  Luke Thorburn, Ariel Kruger
- Machine Coaching with Proxy Coaches 45-64
  Vasileios Theodoros Markos, Marios Thoma, Loizos Michael
- Online Incremental Learning with Abstract Argumentation Frameworks 65-80
  Hamed Ayoobi, Ming Cao, Rineke Verbrugge, Bart Verheij,
- Graph Neural Networks for Algorithm Selection in Abstract Argumentation 81-95
  Jonas Klein, Isabelle Kuhlmann, Matthias Thimm
- Learning Gradual Argumentation Frameworks using Meta-heuristics 96-108
  Nico Potyka, Mohamad Bazo, Jonathan Spieler, Steffen Staab,

2022-09-07: submitted by Jack Mumford, metadata incl. bibliographic data published under Creative Commons CC0
2022-09-10: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|