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

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › 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.

Originalsprog	Engelsk
Titel	Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA '21)
Publikationsdato	2021
Artikelnummer	11
DOI	https://doi.org/10.48550/arXiv.2103.16168
Status	Udgivet - 2021

Emneord

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

Adgang til dokumentet

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

Citationsformater

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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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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

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KW - AI-driven applications

KW - Player-AI interaction

KW - Explainable AI

U2 - 10.48550/arXiv.2103.16168

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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

Abstract

Emneord

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Citationsformater