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Why Should We Gender?: The Effect of Robot Gendering and Occupational Stereotypes on Human Trust and Perceived Competency

Authors:

De'Aira Bryant,

Jason Borenstein,

Ayanna HowardAuthors Info & Claims

HRI '20: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

Pages 13 - 21

https://doi.org/10.1145/3319502.3374778

Published: 09 March 2020 Publication History

Abstract

The attribution of human-like characteristics onto humanoid robots has become a common practice in Human-Robot Interaction by designers and users alike. Robot gendering, the attribution of gender onto a robotic platform via voice, name, physique, or other features is a prevalent technique used to increase aspects of user acceptance of robots. One important factor relating to acceptance is user trust. As robots continue to integrate themselves into common societal roles, it will be critical to evaluate user trust in the robot's ability to perform its job. This paper examines the relationship among occupational gender-roles, user trust and gendered design features of humanoid robots. Results from the study indicate that there was no significant difference in the perception of trust in the robot's competency when considering the gender of the robot. This expands the findings found in prior efforts that suggest performance-based factors have larger influences on user trust than the robot's gender characteristics. In fact, our study suggests that perceived occupational competency is a better predictor for human trust than robot gender or participant gender. As such, gendering in robot design should be considered critically in the context of the application by designers. Such precautions would reduce the potential for robotic technologies to perpetuate societal gender stereotypes.

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Cited By

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https://doi.org/10.3917/anpsy1.241.0101
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https://doi.org/10.3758/s13428-024-02434-z
Barfield J(2024)The Role of Name, Origin, and Voice Accent in a Robot’s Ethnic IdentitySensors10.3390/s2419642124:19(6421)Online publication date: 4-Oct-2024
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Index Terms

Why Should We Gender?: The Effect of Robot Gendering and Occupational Stereotypes on Human Trust and Perceived Competency
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Conferences

HRI '20: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

March 2020

690 pages

ISBN:9781450367462

DOI:10.1145/3319502

General Chairs:
Tony Belpaeme
Ghent University, Belgium
,
James Young
University of Manitoba, Canada
,
Program Chairs:
Hatice Gunes
University of Cambridge, UK
,
Laurel Riek
UC San Diego, USA

Copyright © 2020 ACM.

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SIGAI: ACM Special Interest Group on Artificial Intelligence
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Published: 09 March 2020

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NSF Graduate Research Fellowship Program
Linda J. and Mark C. Smith Endowed Chair in Bioengineering at the Georgia Institute of Technology
National Science Foundation
Alfred P. Sloan Foundation

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HRI '20

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HRI '20: ACM/IEEE International Conference on Human-Robot Interaction

March 23 - 26, 2020

Cambridge, United Kingdom

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Cited By

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https://doi.org/10.3917/anpsy1.241.0101
Marchesi SDe Tommaso DKompatsiari KWu YWykowska A(2024)Tools and methods to study and replicate experiments addressing human social cognition in interactive scenariosBehavior Research Methods10.3758/s13428-024-02434-z56:7(7543-7560)Online publication date: 23-May-2024
https://doi.org/10.3758/s13428-024-02434-z
Barfield J(2024)The Role of Name, Origin, and Voice Accent in a Robot’s Ethnic IdentitySensors10.3390/s2419642124:19(6421)Online publication date: 4-Oct-2024
https://doi.org/10.3390/s24196421
Längle SSchlögl SEcker Avan Kooten WSpieß T(2024)Nonbinary Voices for Digital Assistants—An Investigation of User Perceptions and Gender StereotypesRobotics10.3390/robotics1308011113:8(111)Online publication date: 23-Jul-2024
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Mays K(2024)Authentic Impediments: The Influence of Identity Threat, Cultivated Perceptions, and Personality on RobophobiaHuman-Machine Communication10.30658/hmc.8.108(205-226)Online publication date: 2024
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Duan WMcNeese NFreeman GLi L(2024)Mitigating Gender Stereotypes Toward AI Agents Through an eXplainable AI (XAI) ApproachProceedings of the ACM on Human-Computer Interaction10.1145/36869698:CSCW2(1-35)Online publication date: 8-Nov-2024
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Ren YClement J(2024)Augmenting Human Teams with Robots in Knowledge Work Settings: Insights from the LiteratureACM Transactions on Human-Robot Interaction10.1145/364988413:2(1-34)Online publication date: 14-Jun-2024
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De Cet MCvajner MTorre IObaid M(2024)Do Your Expectations Match? A Mixed-Methods Study on the Association Between a Robot's Voice and AppearanceProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665551(1-11)Online publication date: 8-Jul-2024
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Fujii TSeaborn KSteeds M(2024)Silver-Tongued and Sundry: Exploring Intersectional Pronouns with ChatGPTProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642303(1-14)Online publication date: 11-May-2024
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Ye XJo WAli ABhatti SEsterwood CKassie HRobert LGrollman DBroadbent EJu WSoh HWilliams T(2024)Autonomy Acceptance Model (AAM): The Role of Autonomy and Risk in Security Robot AcceptanceProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3635005(840-849)Online publication date: 11-Mar-2024
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