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Contribution Details

Type Book Chapter
Scope Contributions to practice
Title Female by Default? – Exploring the Effect of Voice Assistant Gender and Pitch on Trait and Trust Attribution
Organization Unit
  • Contribution from another University/Organization than University of Zurich
  • Suzanne Tolmeijer
  • Naim Zierau
  • Andreas Janson
  • Jalil Sebastian Wahdatehagh
  • Jan Marco Marco Leimeister
  • Abraham Bernstein
  • Yoshifumi Kitamura
  • et al
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Booktitle CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
ISBN 978-1-4503-8095-9
Place of Publication New York, NY, USA
Publisher ACM
Page Range Art. 455
Date 2021
Abstract Text Gendered voice based on pitch is a prevalent design element in many contemporary Voice Assistants(VAs) but has shown to strengthen harmful stereotypes. Interestingly, there is a dearth of research that systematically analyses user perceptions of different voice genders in VAs. This study investigates gender-stereotyping across two different tasks by analyzing the influence of pitch (low, high) and gender (women, men) on stereotypical trait ascription and trust formation in an exploratory online experiment with 234 participants. Additionally, we deploy a gender-ambiguous voice to compare against gendered voices. Our findings indicate that implicit stereotyping occurs for VAs. Moreover, we can show that there are no significant differences in trust formed towards a gender-ambiguous voice versus gendered voices, which highlights their potential for commercial usage.
Related URLs
Digital Object Identifier 10.1145/3411763.3451623
Other Identification Number merlin-id:21086
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