Understanding Mental Models of AI through Player-AI Interaction

Jichen Zhu; Jennifer  Villareale

Understanding Mental Models of AI through Player-AI Interaction

Jichen Zhu, Jennifer Villareale

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

Designing human-centered AI-driven applications require deep understandings of how people develop mental models of AI. Currently, we have little knowledge of this process and limited tools to study it. This paper presents the position that AI-based games, particularly the player-AI interaction component, offer an ideal domain to study the process in which mental models evolve. We present a case study to illustrate the beneﬁts of our approach for explainable AI.

Original language	English
Title of host publication	Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA '21)
Publication date	2021
Article number	11
Publication status	Published - 2021

Access to Document

2103.16168(3)

Cite this

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title = "Understanding Mental Models of AI through Player-AI Interaction",

abstract = "Designing human-centered AI-driven applications require deep understandings of how people develop mental models of AI. Currently, we have little knowledge of this process and limited tools to study it. This paper presents the position that AI-based games, particularly the player-AI interaction component, offer an ideal domain to study the process in which mental models evolve. We present a case study to illustrate the beneﬁts of our approach for explainable AI.",

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PY - 2021

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AB - Designing human-centered AI-driven applications require deep understandings of how people develop mental models of AI. Currently, we have little knowledge of this process and limited tools to study it. This paper presents the position that AI-based games, particularly the player-AI interaction component, offer an ideal domain to study the process in which mental models evolve. We present a case study to illustrate the beneﬁts of our approach for explainable AI.

M3 - Article in proceedings

BT - Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA '21)

ER -