| Vol-2894 urn:nbn:de:0074-2894-9 Copyright © 2021 for the individual papers by the papers' authors. Copyright © 2021 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).

# SICSA XAI 2021
SICSA XAI Workshop 2021

### Proceedings of the SICSA eXplainable Artifical Intelligence Workshop 2021

### Aberdeen, United Kingdom, June 1st, 2021.

Edited by

### Kyle Martin *
Nirmalie Wiratunga *
Anjana Wijekoon *

* Robert Gordon University, AIR Research Group, School of Computing, Garthdee House, Garthdee Road, Aberdeen, Scotland

## Table of Contents

- Preface
  Summary: There were 13 papers submitted for peer-review to this workshop. Of these submissions, 12 papers were accepted for presentation at the workshop and 11 papers are presented in this volume. During the workshop, paper presentations were recorded with authors permission and are available at the following link.

### Session 1: Applying and Evaluating Explanations

- Counterfactual Explanations for Student Outcome Prediction with Moodle Footprints 1 - 8
  Anjana Wijekoon, Nirmalie Wiratunga, Ikechukwu Nkisi-Orji, Kyle Martin, Chamath Palihawadana, David Corsar
- Are Contrastive Explanations Useful? 9 - 16
  James Forrest, Somayajulu Sripada, Wei Pang, George M. Coghill
- I don't understand! Evaluation Methods for Natural Language Explanations 17 - 24
  Miruna Clinciu, Arash Eshghi, Helen Hastie

### Session 2: Roles within an XAI System and Accountability

- Towards Accountability Driven Development for Machine Learning Systems 25 - 32
  Chiu Pang Fung, Wei Pang, Iman Naja, Milan Markovic, Peter Edwards
- On Evidence Capture for Accountable AI Systems 33 - 39
  Wei Pang, Milan Markovic, Iman Naja, Chiu Pang Fung, Peter Edwards
- Challenges and Future Directions for Accountable Machine Learning 40 - 47
  Agne Zainyte, Wei Pang
- Human-in-the-Loop Approach to Digitisation of Engineering Drawings 48 - 55
  Andrew M. Fagan, Graeme M. West, Stephen D. J. McArthur

### Session 3: Searching for Explanations

- Explaining Pen and Paper Puzzles with MUSes? 56 - 63
  Joan Espasa, Ian P. Gent, Ruth Hoffmann, Christopher Jefferson, Matthew J. McIlree, Alice M. Lynch
- Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation 64 - 70
  Alexander E. I. Brownlee, Aidan Wallace, David Cairns

### Session 4: Position Papers

- Use of Explainable AI to Refine Artificial Immune System Algorithms 71 - 74
  Rachana R. Patel
- Non-Deterministic Solvers and Explainable AI through Trajectory Mining 75 - 78
  Martin Fyvie, John A. W. McCall, Lee A. Christie

2021-06-18: submitted by Kyle Martin, metadata incl. bibliographic data published under Creative Commons CC0
2021-07-02: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|