research-article

The Effects of Warmth and Competence Perceptions on Users' Choice of an AI System

Authors:

Liat LevontinAuthors Info & Claims

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

Article No.: 583, Pages 1 - 13

https://doi.org/10.1145/3411764.3446863

Published: 07 May 2021 Publication History

Abstract

People increasingly rely on Artificial Intelligence (AI) based systems to aid decision-making in various domains and often face a choice between alternative systems. We explored the effects of users' perception of AI systems' warmth (perceived intent) and competence (perceived ability) on their choices. In a series of studies, we manipulated AI systems' warmth and competence levels. We show that, similar to the judgments of other people, there is often primacy for warmth over competence. Specifically, when faced with a choice between a high-competence system and a high-warmth system, more participants preferred the high-warmth system. Moreover, the precedence of warmth persisted even when the high-warmth system was overtly deficient in its competence compared to an alternative high competence-low warmth system. The current research proposes that it may be vital for AI systems designers to consider and communicate the system's warmth characteristics to its potential users.

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Index Terms

The Effects of Warmth and Competence Perceptions on Users' Choice of an AI System
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

Index terms have been assigned to the content through auto-classification.

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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

General Chairs:
Yoshifumi Kitamura
Tohoku University, Japan
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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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Published: 07 May 2021

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https://doi.org/10.1016/j.tele.2025.102262
Chen JZhang YLiu Z(2025)Unlocking the power of algorithmic recommendations: the effect of recommendation characteristics on users’ willingness to value co-creationCurrent Psychology10.1007/s12144-024-07175-yOnline publication date: 9-Jan-2025
https://doi.org/10.1007/s12144-024-07175-y
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