research-article

Effects of Communication Directionality and AI Agent Differences in Human-AI Interaction

Authors:

Zahra Ashktorab,

Sadhana Kumaravel,

Murray CampbellAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 238, Pages 1 - 15

https://doi.org/10.1145/3411764.3445256

Published: 07 May 2021 Publication History

Abstract

In Human-AI collaborative settings that are inherently interactive, direction of communication plays a role in how users perceive their AI partners. In an AI-driven cooperative game with partially observable information, players (be it the AI or the human player) require their actions to be interpreted accurately by the other player to yield a successful outcome. In this paper, we investigate social perceptions of AI agents with various directions of communication in a cooperative game setting. We measure subjective social perceptions (rapport, intelligence, and likeability) of participants towards their partners when participants believe they are playing with an AI or with a human and the nature of the communication (responsiveness and leading roles). We ran a large scale study on Mechanical Turk (n=199) of this collaborative game and find significant differences in gameplay outcome and social perception across different AI agents, different directions of communication and when the agent is perceived to be an AI/Human. We find that the bias against the AI that has been demonstrated in prior studies varies with the direction of the communication and with the AI agent.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

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Yoshifumi Kitamura
Tohoku University, Japan
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Aaron Quigley
University of New South Wales, Australia
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University of California Santa Cruz, USA
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Takeo Igarashi
The University of Tokyo, Japan
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Pernille Bjørn
University of Copenhagen, Denmark
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Steven Drucker
Microsoft Research, USA

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https://dl.acm.org/doi/10.1145/3701571.3701582
Do HBrachman MDugan CJohnson JLauer JRai PPan Q(2024)Grounding with Structure: Exploring Design Variations of Grounded Human-AI Collaboration in a Natural Language InterfaceProceedings of the ACM on Human-Computer Interaction10.1145/36869028:CSCW2(1-27)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686902
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