Explainable AI for Designers

Jichen Zhu, Rafael Bidarra, Alex J Champandard, Simon Colton, Reynald Francois, Matthew J Guzdial, Amy K Hoover, Antonios Liapis, Sebastian Risi, Gillian Smith, Anne Sullivan, G Michael Youngblood
Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Volume 7, page 125 - 2017
Download the publication : XAID.Dagstuhl.2017.pdf [257Ko]  
In response to the rapid technological success in AI, the emerging research area of Explainable AI aims to better communicate AI systems' decisions and actions to human users. The central goals of explainable AI are often to increase users' understanding, foster trust, and improve their ability to utilize the systems. Explainable AI for designers (XAID), in particular, focuses on enhancing designers' capability to (co-)create user experiences with AI. Through the vantage point of computer games, we examine 1) the design space of explainable AI for game designers, 2) three case studies of XAIDs, and 3) design guidelines and open challenges in each case.

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BibTex references

@InBook { ZBCCFGHLRSSY17,
  author       = "Zhu, Jichen and Bidarra, Rafael and Champandard, Alex J and Colton, Simon and Francois, Reynald and Guzdial, Matthew J and Hoover, Amy K and Liapis, Antonios and Risi, Sebastian and Smith, Gillian and Sullivan, Anne and Youngblood, G Michael",
  title        = "Explainable AI for Designers",
  series       = "Artificial and Computational Intelligence in Games: AI-Driven Game Design (Dagstuhl Seminar 17471)",
  volume       = "7",
  pages        = "125",
  year         = "2017",
  publisher    = "Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik",
  url          = "http://graphics.tudelft.nl/Publications-new/2017/ZBCCFGHLRSSY17"
}

Other publications in the database

» Jichen Zhu
» Rafael Bidarra
» Antonios Liapis
» Sebastian Risi
» Gillian Smith
» Anne Sullivan
» G Michael Youngblood






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