Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation

Jichen Zhu, Antonios Liapis, Sebastian Risi, Rafael Bidarra, G. Michael Youngblood

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

40 Citations (Scopus)
21 Downloads (Pure)

Abstract

Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for Designers (XAID), specifically for game designers. By focusing on a specific user group, their needs and tasks, we propose a human-centered approach for facilitating game designers to co-create with AI/ML techniques through XAID. We illustrate our initial XAID framework through three use cases, which require an understanding both of the innate properties of the AI techniques and users' needs, and we identify key open challenges.

Original languageEnglish
Title of host publication2018 IEEE Conference on Computational Intelligence and Games ( CIG)
Subtitle of host publicationProceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-5386-4359-4
ISBN (Print)978-1-5386-4360-0
DOIs
Publication statusPublished - 2018
Event14th IEEE Conference on Computational Intelligence and Games, CIG 2018 - Maastricht, Netherlands
Duration: 14 Aug 201817 Aug 2018

Conference

Conference14th IEEE Conference on Computational Intelligence and Games, CIG 2018
CountryNetherlands
CityMaastricht
Period14/08/1817/08/18

Keywords

  • Explainable artificial intelligence
  • Game design
  • Human-computer interaction
  • Machine learning
  • Mixed-initiative co-creation

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