short-paper

Measuring Subconscious Gender Biases against Male and Female Virtual Agents in Japan

Authors:

Miki TakaseAuthors Info & Claims

HAI '22: Proceedings of the 10th International Conference on Human-Agent Interaction

Pages 275 - 277

https://doi.org/10.1145/3527188.3563909

Published: 05 December 2022 Publication History

Abstract

This study aimed to investigate whether Japanese participants have subconscious gender biases against a male and a female virtual agents and quantify those biases if any. The participants were given a negative feedback after taking a logical thinking test regardless of their performance by the virtual agents. Then we measured their subconscious gender biases with IAT. The preliminary results indicated the participants showed general gender biases against the male agent. On the other hand, stereotypical gender biases strongly appeared against the female agent. These results provide implications for more gender-neutral agent design in applications where they might give negative feedbacks such as education and training.

References

[1]

Bilge Karacora, Morteza Dehghani, Nicole Krämer, Jonathan Gratch, 2012. The influence of virtual agents’ gender and rapport on enhancing math performance. In Proceedings of the 34th Annual Meeting of the Cognitive Science Society.

[2]

Krämer, N. C., Karacora, B., Lucas, G., Dehghani, M., Rüther, G., & Gratch, J., 2016. Closing the gender gap in STEM with friendly male instructors? On the effects of rapport behavior and gender of a virtual agent in an instructional interaction. Computers & Education, 99, 1-13.

Digital Library

[3]

Jeong, D.C., Feng, D., Krämer, N.C., Miller, L.C., Marsella, S., 2017. Negative Feedback In Your Face: Examining the Effects of Proxemics and Gender on Learning. In: Beskow, J., Peters, C., Castellano, G., O'Sullivan, C., Leite, I., Kopp, S. (eds) Proceedings of Intelligent Virtual Agents. IVA 2017. Lecture Notes in Computer Science, vol 10498. 170–183. Springer, Cham.

[4]

Mell, J., Lucas, G., Gratch, J., 2017. Prestige Questions, Online Agents, and Gender-Driven Differences in Disclosure. In: Beskow, J., Peters, C., Castellano, G., O'Sullivan, C., Leite, I., Kopp, S. (eds) Proceedings on Intelligent Virtual Agents. IVA 2017. Lecture Notes in Computer Science, vol 10498, 273–282, Springer, Cham.

[5]

Timothy Bickmore, Dhaval Parmar, Everlyne Kimani, Stefan Olafsson, 2021. Diversity Informatics: Reducing Racial and Gender Bias with Virtual Agents. In Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents, 25–32.

Digital Library

[6]

N. A. Palomares and E.-J. Lee, 2010. Virtual gender identity: The linguistic assimilation to gendered avatars in computer-mediated communication. In Journal of Language and Social Psychology, 29(1):5–23.

[7]

N. Yee, N. Ducheneaut, M. Yao, and L. Nelson, 2011. Do men heal more when in drag? Conflicting identity cues between user and avatar. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 773–776.

Digital Library

[8]

Yanghee Kim, A.L. Baylor, & E. shen, 2007. Pedagogical agents as learning companions: the impact of agent emotion and gender, Journal of Computer Assisted Learning, 23, 220-234.

[9]

M. Siegel, C. Breazeal, and M. I. Norton. 2009. Persuasive robotics: The influence of robot gender on human behavior. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2563–2568.

[10]

Feng, D., Jeong, D.C., Krämer, N.C., Miller, L.C., & Marsella, S., 2017. "Is It Just Me?": Evaluating Attribution of Negative Feedback as a Function of Virtual Instructor's Gender and Proxemics. In AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and Multi Agent Systems. 810–818.

[11]

Amodio, D. M., Devine, P. G., 2006. Stereotyping and evaluation in implicit race bias: Evidence for independent constructs and unique effects on behavior, Journal of Personality and Social Psychology, 91, 652-661.

[12]

Petty, R. E., Fazio, R. H., & Briñol, P., 2009. (Eds.): Attitudes: Insights from the new implicit measures, New York: Psychology Press.

[13]

World Economic Forum: Global Gender Gap Report 2020, https://www.weforum.org/reports/gender-gap-2020-report-100-years-pay-equality (Retrieved 2022/5/12)

[14]

World Economic Forum: Global Gender Gap Report 2021, https://www.weforum.org/reports/global-gender-gap-report-2021 (Retrieved 2022/5/12)

[15]

Ishii, Kunio, Numazaki, Makoto, 2012. The effects of threat to self-worth on implicit prejudice of men toward women, Japanese journal of interpersonal and social psychology, 12, 67-76.

[16]

Karpinski, A., Steinman,R. B., 2006. The single category implicit association test as a measure of implicit social cognition, Journal of Personality and Social Psychology, 91, 16-32.

[17]

Brent Rossen, Kyle Johnsen, Adeline Deladisma, Scott Lind & Benjamin Lok, 2008. Virtual Humans Elicit Skin-Tone Bias Consistent with Real-World Skin-Tone Biases. In Proceedings of 8th International Conference on Intelligent Virtual Agents, 237–244.

Digital Library

[18]

Dovidio, J. F., Kawakami, K., and Gaertner, S. L., 2002. Implicit and Explicit Prejudice and Interracial Interaction, Journal of Personality and Social Psychology, 82, 1, 62-68.

[19]

Nosek, B., Mahzarin, B., and Greenwald, A., 2002. Harvesting Implicit Group Attitudes and Beliefs from a Demonstration Web Site, Group Dynamics: Theory, Research, and Practice, 6, 1, 101-115.

[20]

Procheta Nag, Ozge Nilay Yalcin,2020. Gender Stereotypes in Virtual Agents. in Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, No.41, pp.1-8.

Digital Library

[21]

Greenwald, A. G., & Banaji, M. R. 1995. Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102(1), 4–27. https://doi.org/10.1037/0033-295X.102.1.4.

[22]

Rabia Khan and Antonella De Angeli. 2009. The attractiveness stereotype in the evaluation of embodied conversational agents. In IFIP Conference on Human-Computer Interaction. Springer, 85-97.

Digital Library

[23]

Catherine Pelachaud. 2015. Greta: An Interactive Expressive Embodied Conversational Agent. In Proc. of the 2015 International Conference on Autonomous Agents and Multiagent Systems.

[24]

Christiana Tsiourti, Emilie Joly, Cindy Wings, Maher Ben Moussa, and Katarzyna Wac. 2014. Virtual Assistive Companions for Older Adults: Qualitative Field Study and Design Implications. In Proc. of the 8th International Conference on Pervasive Computing Technologies for Healthcare. ICST.

Digital Library

[25]

Timothy Bickmore, Everlyne Kimani, Ha Trinh, Alexandra Pusateri, Michael Paasche-Orlow, and Jared Magnani. 2018. Managing Chronic Conditions with a Smartphone-based Conversational Virtual Agent. In Proc. of the 18th Int. Conference on intelligent virtual agents (IVA ’18). ACM, 119-124.

Digital Library

[26]

Timothy W Bickmore, Daniel Schulman, and Candace Sidner. 2013. Automated interventions for multiple health behaviors using conversational agents. Patient education and counseling 92.

[27]

Daniel Schulman, Timothy Bickmore, and Candace Sidner. 2011. An Intelligent Conversational Agent for Promoting Long-Term Health Behavior Change Using Motivational Interviewing. In 2011 AAAI Spring Symposium Series.

[28]

Ha Trinh, Ameneh Shamekhi, Everlyne Kimani, and Timothy Bickmor. 2018. Predicting User Engagement in Longitudinal Interventions with Virtual Agents. In Proc. of the 18th Int. Conf. on Intelligent Virtual Agents (IVA ’18). ACM, 9-16.

Digital Library

[29]

Lazlo Ring, Timothy Bickmore, and Paola Pedrelli. 2016. An Affectively Aware Virtual Therapist for Depression Counseling. In ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) Workshop on Computing and Mental Health.

[30]

Amy Baylor and Suzanne J. Ebbers. 2003. Evidence That Multiple Agents Facilitate Greater Learning. Artificial intelligence in education: Shaping the future of learning through intelligent technologies, 377-379.

[31]

Amy Baylor, Jeeheon Ryu, and E. Shen. 2003. The Effects of Pedagogical Agent Voice and Animation on Learning, Motivation and Perceived Persona. In EdMedia+Innovate Learning. Association for the Advancement of Computing in Education (AACE), 452-458.

[32]

S Kopp, L Gesellensetter, NC Kramer, and L Wachsmuth. 2005. A conversational agent as museum guide - Design and evaluation of a real-world application. Intelligent Virtual Agents, Proceedings 3661, 329-343.

Digital Library

[33]

Dominic W. Massaro. 2004. Symbiotic Value of an Embodied Agent in Language Learning. In 37th Annual Hawaii International Conference on System Sciences. Proceedings of The. IEEE.

[34]

Preben Wik and Anna Hjalmarsson. 2009. Embodied conversational agents in computer assisted language learning. Speech Communication 51, 10, 1024-1037. http://search.proquest.com/docview/85705901/

Digital Library

Cited By

Ghosh RFeijóo-García PStuart JWrenn CLok B(2023)Evaluating face gender cues in virtual humans within and beyond the gender binaryFrontiers in Virtual Reality10.3389/frvir.2023.12514204Online publication date: 24-Aug-2023
https://doi.org/10.3389/frvir.2023.1251420
Parlangeli OPalmitesta PMasi LTittarelli MGuidi S(2023)It’s a Long Way to Neutrality. An Evaluation of Gendered Artificial FacesDesign, User Experience, and Usability10.1007/978-3-031-35708-4_27(366-378)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35708-4_27

Index Terms

Measuring Subconscious Gender Biases against Male and Female Virtual Agents in Japan
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

Gender Stereotypes in Virtual Agents
IVA '20: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

Visual, behavioral and verbal cues for gender are often used in designing virtual agents to take advantage of their stereotypical effects on the users. However, recent studies point towards a more gender-balanced view of stereotypical traits and roles ...
The perseverance of gender stereotype
HCI '18: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Numerous studies have observed unequal representation of gender stereotypes across different areas. However, some of the studies featured in this body of research were focused on using direct measures of implicit associations to understand how students ...
Reducing Implicit Gender Bias Using a Virtual Workplace Environment
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

Implicit gender bias has costly and complex consequences for women in the workplace. We present an online desktop virtual environment that follows the story of a male or female self-avatar from the first-person perspective, who either experiences a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

HAI '22: Proceedings of the 10th International Conference on Human-Agent Interaction

December 2022

352 pages

ISBN:9781450393232

DOI:10.1145/3527188

Editors:
Christoph Bartneck
University of Canterbury, New Zealand
,
Takayuki Kanda
Kyoto University, Japan
,
Mohammad Obaid
Chalmers University of Technology, Sweden
,
Wafa Johal
University of Melbourne, Australia

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 December 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

JSPS KAKENHI

Conference

HAI '22

HAI '22: International Conference on Human-Agent Interaction

December 5 - 8, 2022

Christchurch, New Zealand

Acceptance Rates

Overall Acceptance Rate 121 of 404 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
98
Total Downloads

Downloads (Last 12 months)36
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ghosh RFeijóo-García PStuart JWrenn CLok B(2023)Evaluating face gender cues in virtual humans within and beyond the gender binaryFrontiers in Virtual Reality10.3389/frvir.2023.12514204Online publication date: 24-Aug-2023
https://doi.org/10.3389/frvir.2023.1251420
Parlangeli OPalmitesta PMasi LTittarelli MGuidi S(2023)It’s a Long Way to Neutrality. An Evaluation of Gendered Artificial FacesDesign, User Experience, and Usability10.1007/978-3-031-35708-4_27(366-378)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35708-4_27

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten