Understanding Mental Models of AI through Player-AI Interaction

Jichen Zhu; Jennifer  Villareale

doi:10.48550/arXiv.2103.16168

Understanding Mental Models of AI through Player-AI Interaction

Jichen Zhu, Jennifer Villareale

Procedural eXpression Lab

Drexel University

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
DOIs	https://doi.org/10.48550/arXiv.2103.16168
Publication status	Published - 2021

Keywords

Human-centered AI
Mental models
AI-driven applications
Player-AI interaction
Explainable AI

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10.48550/arXiv.2103.16168

2103.16168(3)

https://www.researchgate.net/publication/350512179_Understanding_Mental_Models_of_AI_through_Player-AI_Interaction/fulltext/6063ea95a6fdccbfea1a5c4f/Understanding-Mental-Models-of-AI-through-Player-AI-Interaction.pdf?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19

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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author = "Jichen Zhu and Jennifer Villareale",

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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.

KW - Human-centered AI

KW - Mental models

KW - AI-driven applications

KW - Player-AI interaction

KW - Explainable AI

KW - Human-centered AI

KW - Mental models

KW - AI-driven applications

KW - Player-AI interaction

KW - Explainable AI

U2 - 10.48550/arXiv.2103.16168

DO - 10.48550/arXiv.2103.16168

M3 - Article in proceedings

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

ER -

Understanding Mental Models of AI through Player-AI Interaction

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Keywords

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