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: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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)
Number of pages8
Publication date2018
Publication statusPublished - 2018


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