Designing Robot Identity: The Role of Voice, Clothing, and Task on Robot Gender Perception (2404.00494v2)
Abstract: Perceptions of gender are a significant aspect of human-human interaction, and gender has wide-reaching social implications for robots deployed in contexts where they are expected to interact with humans. This work explored two flexible modalities for communicating gender in robots--voice and appearance--and we studied their individual and combined influences on a robot's perceived gender. We evaluated the perception of a robot's gender through three video-based studies. First, we conducted a study (n=65) on the gender perception of robot voices by varying speaker identity and pitch. Second, we conducted a study (n=93) on the gender perception of robot clothing designed for two different tasks. Finally, building on the results of the first two studies, we completed a large integrative video-based study (n=273) involving two human-robot interaction tasks. We found that voice and clothing can be used to reliably establish a robot's perceived gender, and that combining these two modalities can have different effects on the robot's perceived gender. Taken together, these results inform the design of robot voices and clothing as individual and interacting components in the perceptions of robot gender.
- A. K. Pandey and R. Gelin, “A mass-produced sociable humanoid robot: Pepper: The first machine of its kind,” IEEE Robotics & Automation Magazine, vol. 25, no. 3, pp. 40–48, 2018.
- A. Specian, R. Mead, S. Kim, M. Matarić, and M. Yim, “Quori: A community-informed design of a socially interactive humanoid robot,” 2021.
- M. Suguitan and G. Hoffman, “Blossom: A handcrafted open-source robot,” ACM Transactions on Human-Robot Interaction (THRI), vol. 8, no. 1, pp. 1–27, 2019.
- E. C. Deng, B. Mutlu, and M. J. Matarić, “Formalizing the design space and product development cycle for socially interactive robots,” in Workshop on Social Robots in the Wild at the 2018 ACM Conference on Human-Robot Interaction (HRI), 2018.
- C. Nass, J. Steuer, and E. R. Tauber, “Computers are social actors,” in Proceedings of the SIGCHI conference on Human factors in computing systems, 1994, pp. 72–78.
- C. Nass and Y. Moon, “Machines and mindlessness: Social responses to computers,” Journal of social issues, vol. 56, no. 1, pp. 81–103, 2000.
- J. Wainer, D. J. Feil-Seifer, D. A. Shell, and M. J. Mataric, “The role of physical embodiment in human-robot interaction,” in ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 2006, pp. 117–122.
- E. Deng, B. Mutlu, and M. J. Mataric, “Embodiment in socially interactive robots,” Foundations and Trends® in Robotics, vol. 7, no. 4, pp. 251–356, 2019.
- P. Esmaeilzadeh, “How does it identity affect individuals’ use behaviors associated with personal health devices (phds)? an empirical study,” Information & Management, vol. 58, no. 1, p. 103313, 2021.
- M. A. DeVito, A. M. Walker, and J. Birnholtz, “’too gay for facebook’ presenting lgbtq+ identity throughout the personal social media ecosystem,” Proceedings of the ACM on Human-Computer Interaction, vol. 2, no. CSCW, pp. 1–23, 2018.
- H. Tajfel and J. C. Turner, “The social identity theory of intergroup behavior,” in Political psychology. Psychology Press, 2004, pp. 276–293.
- K. White, R. Habib, and D. J. Hardisty, “How to shift consumer behaviors to be more sustainable: A literature review and guiding framework,” Journal of Marketing, vol. 83, no. 3, pp. 22–49, 2019.
- J. Hennessy and M. A. West, “Intergroup behavior in organizations: A field test of social identity theory,” Small group research, vol. 30, no. 3, pp. 361–382, 1999.
- G. Charness and Y. Chen, “Social identity, group behavior, and teams,” Annual Review of Economics, vol. 12, pp. 691–713, 2020.
- J. E. Stets and P. J. Burke, “Identity theory and social identity theory,” Social psychology quarterly, pp. 224–237, 2000.
- M. R. Fraune, “Our robots, our team: Robot anthropomorphism moderates group effects in human–robot teams,” Frontiers in psychology, vol. 11, p. 1275, 2020.
- M. R. Fraune, S. Šabanović, and E. R. Smith, “Teammates first: Favoring ingroup robots over outgroup humans,” in 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE, 2017, pp. 1432–1437.
- M. Häring, D. Kuchenbrandt, and E. André, “Would you like to play with me? how robots’ group membership and task features influence human-robot interaction,” in Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction, 2014, pp. 9–16.
- D. Kuchenbrandt, F. Eyssel, S. Bobinger, and M. Neufeld, “When a robot’s group membership matters: Anthropomorphization of robots as a function of social categorization,” International Journal of Social Robotics, vol. 5, pp. 409–417, 2013.
- S. Sebo, B. Stoll, B. Scassellati, and M. F. Jung, “Robots in groups and teams: a literature review,” Proceedings of the ACM on Human-Computer Interaction, vol. 4, no. CSCW2, pp. 1–36, 2020.
- J. L. Davis, T. P. Love, and P. Fares, “Collective social identity: Synthesizing identity theory and social identity theory using digital data,” Social Psychology Quarterly, vol. 82, no. 3, pp. 254–273, 2019.
- W.-L. Chang, J. P. White, J. Park, A. Holm, and S. Šabanović, “The effect of group size on people’s attitudes and cooperative behaviors toward robots in interactive gameplay,” in 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 2012, pp. 845–850.
- M. R. Fraune, S. Sherrin, S. Šabanović, and E. R. Smith, “Is human-robot interaction more competitive between groups than between individuals?” in 2019 14th acm/ieee international conference on human-robot interaction (hri). IEEE, 2019, pp. 104–113.
- S. Bardzell, “Feminist hci: taking stock and outlining an agenda for design,” in Proceedings of the SIGCHI conference on human factors in computing systems, 2010, pp. 1301–1310.
- S. Stumpf, A. Peters, S. Bardzell, M. Burnett, D. Busse, J. Cauchard, E. Churchill et al., “Gender-inclusive hci research and design: A conceptual review,” Foundations and Trends® in Human–Computer Interaction, vol. 13, no. 1, pp. 1–69, 2020.
- D. Kuchenbrandt, M. Häring, J. Eichberg, F. Eyssel, and E. André, “Keep an eye on the task! how gender typicality of tasks influence human–robot interactions,” International Journal of Social Robotics, vol. 6, pp. 417–427, 2014.
- A. Sandygulova and G. M. O’Hare, “Age-and gender-based differences in children’s interactions with a gender-matching robot,” International Journal of Social Robotics, vol. 10, no. 5, pp. 687–700, 2018.
- S. C. Steinhaeusser, P. Schaper, O. Bediako Akuffo, P. Friedrich, J. Ön, and B. Lugrin, “Anthropomorphize me! effects of robot gender on listeners’ perception of the social robot nao in a storytelling use case,” in Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021, pp. 529–534.
- T. Law, B. F. Malle, and M. Scheutz, “A touching connection: how observing robotic touch can affect human trust in a robot,” International Journal of Social Robotics, pp. 1–17, 2021.
- C. R. Crowelly, M. Villanoy, M. Scheutzz, and P. Schermerhornz, “Gendered voice and robot entities: perceptions and reactions of male and female subjects,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2009, pp. 3735–3741.
- N. Raghunath, C. A. Sanchez, and N. T. Fitter, “Robot comedy (is) special: A surprising lack of bias for gendered robotic comedians,” in International Conference on Social Robotics. Springer, 2022, pp. 663–673.
- N. Raghunath, P. Myers, C. A. Sanchez, and N. T. Fitter, “Women are funny: Influence of apparent gender and embodiment in robot comedy,” in International Conference on Social Robotics. Springer, 2021, pp. 3–13.
- M. Chita-Tegmark, M. Lohani, and M. Scheutz, “Gender effects in perceptions of robots and humans with varying emotional intelligence,” in 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2019, pp. 230–238.
- A. Powers, A. D. Kramer, S. Lim, J. Kuo, S.-l. Lee, and S. Kiesler, “Eliciting information from people with a gendered humanoid robot,” in ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005. IEEE, 2005, pp. 158–163.
- D. Bryant, J. Borenstein, and A. Howard, “Why should we gender? the effect of robot gendering and occupational stereotypes on human trust and perceived competency,” in Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020, pp. 13–21.
- B. Tay, Y. Jung, and T. Park, “When stereotypes meet robots: the double-edge sword of robot gender and personality in human–robot interaction,” Computers in Human Behavior, vol. 38, pp. 75–84, 2014.
- F. Eyssel and F. Hegel, “(s) he’s got the look: Gender stereotyping of robots 1,” Journal of Applied Social Psychology, vol. 42, no. 9, pp. 2213–2230, 2012.
- D. Robben, E. Fukuda, and M. De Haas, “The effect of gender on perceived anthropomorphism and intentional acceptance of a storytelling robot,” in Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, ser. HRI ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 495–499. [Online]. Available: https://doi.org/10.1145/3568294.3580134
- O. Keyes, “The misgendering machines: Trans/hci implications of automatic gender recognition,” Proceedings of the ACM on human-computer interaction, vol. 2, no. CSCW, pp. 1–22, 2018.
- C. West and D. H. Zimmerman, “Doing gender,” Gender & society, vol. 1, no. 2, pp. 125–151, 1987.
- S. Salih, “On judith butler and performativity,” Sexualities and communication in everyday life: A reader, pp. 55–68, 2007.
- G. Aşkın, İ. Saltık, T. E. Boz, and B. A. Urgen, “Gendered actions with a genderless robot: Gender attribution to humanoid robots in action,” International Journal of Social Robotics, pp. 1–17, 2023.
- K. D. Pyke and D. L. Johnson, “Asian american women and racialized femininities: “doing” gender across cultural worlds,” Gender & Society, vol. 17, no. 1, pp. 33–53, 2003.
- N. Dennler, C. Ruan, J. Hadiwijoyo, B. Chen, S. Nikolaidis, and M. Mataric, “Using design metaphors to understand user expectations of socially interactive robot embodiments,” arXiv preprint arXiv:2201.10671, 2022.
- K. Seaborn and A. Frank, “What pronouns for pepper? a critical review of gender/ing in research,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 2022, pp. 1–15.
- E. H. Jung, T. F. Waddell, and S. S. Sundar, “Feminizing robots: User responses to gender cues on robot body and screen,” in Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 2016, pp. 3107–3113.
- S. Y. Neuteboom and M. de Graaf, “Cobbler stick with your reads: People’s perceptions of gendered robots performing gender stereotypical tasks,” arXiv preprint arXiv:2104.06127, 2021.
- G. Trovato, C. Lucho, and R. Paredes, “She’s electric—the influence of body proportions on perceived gender of robots across cultures,” Robotics, vol. 7, no. 3, p. 50, 2018.
- G. Trovato, C. Lucho, F. Eyssel, and J. Bernotat, “The influence of body proportions on perceived gender of robots in latin america,” in International Conference on Love and Sex with Robots. Springer, 2017, pp. 158–168.
- J. Bernotat, F. Eyssel, and J. Sachse, “The (fe) male robot: how robot body shape impacts first impressions and trust towards robots,” International Journal of Social Robotics, vol. 13, pp. 477–489, 2021.
- N. Friedman, K. Love, R. LC, J. E. Sabin, G. Hoffman, and W. Ju, “What robots need from clothing,” in Designing Interactive Systems Conference 2021, 2021, pp. 1345–1355.
- M. West, R. Kraut, and H. Ei Chew, “I’d blush if i could: closing gender divides in digital skills through education,” 2019.
- K. Seaborn, N. P. Miyake, P. Pennefather, and M. Otake-Matsuura, “Voice in human–agent interaction: A survey,” ACM Computing Surveys (CSUR), vol. 54, no. 4, pp. 1–43, 2021.
- S. Tolmeijer, N. Zierau, A. Janson, J. S. Wahdatehagh, J. M. M. Leimeister, and A. Bernstein, “Female by default?–exploring the effect of voice assistant gender and pitch on trait and trust attribution,” in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1–7.
- C. McGinn and I. Torre, “Can you tell the robot by the voice? an exploratory study on the role of voice in the perception of robots,” in 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2019, pp. 211–221.
- J. Cambre and C. Kulkarni, “One voice fits all? social implications and research challenges of designing voices for smart devices,” Proceedings of the ACM on human-computer interaction, vol. 3, no. CSCW, pp. 1–19, 2019.
- E. Roesler, L. Naendrup-Poell, D. Manzey, and L. Onnasch, “Why context matters: the influence of application domain on preferred degree of anthropomorphism and gender attribution in human–robot interaction,” International Journal of Social Robotics, vol. 14, no. 5, pp. 1155–1166, 2022.
- G. Perugia, S. Guidi, M. Bicchi, and O. Parlangeli, “The shape of our bias: Perceived age and gender in the humanoid robots of the abot database,” in 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2022, pp. 110–119.
- G. Perugia, L. Boor, L. van der Bij, O. Rikmenspoel, R. Foppen, and S. Guidi, “Models of (often) ambivalent robot stereotypes: Content, structure, and predictors of robots’ age and gender stereotypes,” in Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 2023, pp. 428–436.
- K. Winkle, D. McMillan, M. Arnelid, K. Harrison, M. Balaam, E. Johnson, and I. Leite, “Feminist human-robot interaction: Disentangling power, principles and practice for better, more ethical hri,” in Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, ser. HRI ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 72–82. [Online]. Available: https://doi.org/10.1145/3568162.3576973
- K. Winkle, G. I. Melsión, D. McMillan, and I. Leite, “Boosting robot credibility and challenging gender norms in responding to abusive behaviour: A case for feminist robots,” in Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021, pp. 29–37.
- A. Galatolo, G. I. Melsión, I. Leite, and K. Winkle, “The right (wo) man for the job? exploring the role of gender when challenging gender stereotypes with a social robot,” International Journal of Social Robotics, pp. 1–15, 2022.
- S. L. Bem, “Bem sex role inventory,” Journal of personality and social psychology, 1981.
- S. J. Sutton, “Gender ambiguous, not genderless: Designing gender in voice user interfaces (vuis) with sensitivity,” in Proceedings of the 2nd conference on conversational user interfaces, 2020, pp. 1–8.
- I. Torre, E. Lagerstedt, N. Dennler, K. Seaborn, I. Leite, and E. Szekély, “Can a gender-ambiguous voice reduce gender stereotypes in human-robot interactions?” in 2023 32nd IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE, 2023.
- G. Perugia, A. Rossi, and S. Rossi, “Gender revealed: Evaluating the genderedness of furhat’s predefined faces,” in International Conference on Social Robotics. Springer, 2021, pp. 36–47.
- H. L. Bradwell, G. E. A. Noury, K. J. Edwards, R. Winnington, S. Thill, and R. B. Jones, “Design recommendations for socially assistive robots for health and social care based on a large scale analysis of stakeholder positions: Social robot design recommendations,” Health Policy and Technology, vol. 10, no. 3, p. 100544, 2021.
- S. Shiotani, T. Tomonaka, K. Kemmotsu, S. Asano, K. Oonishi, and R. Hiura, “World’s first full-fledged communication robot” wakamaru” capable of living with family and supporting persons,” Mitsubishi Juko Giho, vol. 43, no. 1, pp. 44–45, 2006.
- J. Gay, J. C. Pepusch, and W. Nicholson, “Polly,” 2024. [Online]. Available: https://aws.amazon.com/polly/
- C. R. Pernet and P. Belin, “The role of pitch and timbre in voice gender categorization,” Frontiers in psychology, vol. 3, p. 23, 2012.
- D. A. Puts, S. J. Gaulin, and K. Verdolini, “Dominance and the evolution of sexual dimorphism in human voice pitch,” Evolution and human behavior, vol. 27, no. 4, pp. 283–296, 2006.
- V. Cartei and D. Reby, “Effect of formant frequency spacing on perceived gender in pre-pubertal children’s voices,” PLoS One, vol. 8, no. 12, p. e81022, 2013.
- J. Cambre, J. Colnago, J. Maddock, J. Tsai, and J. Kaye, “Choice of voices: A large-scale evaluation of text-to-speech voice quality for long-form content,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1–13.
- M. K. Scheurman, K. Spiel, O. L. Haimson, F. Hamidi, and S. M. Branham, “Hci gender guidelines,” May 2020. [Online]. Available: https://www.morgan-klaus.com/gender-guidelines.html
- E. Cha, Y. Kim, T. Fong, M. J. Mataric et al., “A survey of nonverbal signaling methods for non-humanoid robots,” Foundations and Trends® in Robotics, vol. 6, no. 4, pp. 211–323, 2018.
- D. Szafir and D. A. Szafir, “Connecting human-robot interaction and data visualization,” in Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021, pp. 281–292.
- L. Palumbo, N. Ruta, and M. Bertamini, “Comparing angular and curved shapes in terms of implicit associations and approach/avoidance responses,” PloS one, vol. 10, no. 10, p. e0140043, 2015.
- R. de Kervenoael, R. Hasan, A. Schwob, and E. Goh, “Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots,” Tourism Management, vol. 78, p. 104042, 2020.
- A. L. Darnell and A. Tabatabai, “The werk that remains: Drag and the mining of the idealized female form,” in RuPaul’s Drag Race and the Shifting Visibility of Drag Culture. Springer, 2017, pp. 91–101.
- M. E. Swinker and J. D. Hines, “Understanding consumers’ perception of clothing quality: A multidimensional approach,” International journal of consumer studies, vol. 30, no. 2, pp. 218–223, 2006.
- J. K. Mogilski and L. L. Welling, “The relative contribution of jawbone and cheekbone prominence, eyebrow thickness, eye size, and face length to evaluations of facial masculinity and attractiveness: A conjoint data-driven approach,” Frontiers in psychology, vol. 9, p. 2428, 2018.
- T. Nomura, T. Suzuki, T. Kanda, and K. Kato, “Measurement of negative attitudes toward robots,” Interaction Studies, vol. 7, no. 3, pp. 437–454, 2006.
- C. M. Carpinella, A. B. Wyman, M. A. Perez, and S. J. Stroessner, “The robotic social attributes scale (rosas) development and validation,” in Proceedings of the 2017 ACM/IEEE International Conference on human-robot interaction, 2017, pp. 254–262.
- M. Paetzel, C. Peters, I. Nyström, and G. Castellano, “Effects of multimodal cues on children’s perception of uncanniness in a social robot,” in Proceedings of the 18th ACM International Conference on Multimodal Interaction, 2016, pp. 297–301.
- W. J. Mitchell, K. A. Szerszen Sr, A. S. Lu, P. W. Schermerhorn, M. Scheutz, and K. F. MacDorman, “A mismatch in the human realism of face and voice produces an uncanny valley,” i-Perception, vol. 2, no. 1, pp. 10–12, 2011.
- J. Li, W. Ju, and C. Nass, “Observer perception of dominance and mirroring behavior in human-robot relationships,” in 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2015, pp. 133–140.
- J. Reinhardt, A. Pereira, D. Beckert, and K. Bengler, “Dominance and movement cues of robot motion: A user study on trust and predictability,” in 2017 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, 2017, pp. 1493–1498.
- R. C.-S. Chang, H.-P. Lu, and P. Yang, “Stereotypes or golden rules? exploring likable voice traits of social robots as active aging companions for tech-savvy baby boomers in taiwan,” Computers in Human Behavior, vol. 84, pp. 194–210, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0747563218300839
- N. T. Fitter, M. Strait, E. Bisbee, M. J. Mataric, and L. Takayama, “You’re wigging me out! is personalization of telepresence robots strictly positive?” in Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021, pp. 168–176.
- Z. Shi, H. Chen, A.-M. Velentza, S. Liu, N. Dennler, A. O’Connell, and M. Mataric, “Evaluating and personalizing user-perceived quality of text-to-speech voices for delivering mindfulness meditation with different physical embodiments,” in Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 2023, pp. 516–524.
- K. Winkle, E. Lagerstedt, I. Torre, and A. Offenwanger, “15 years of (who) man robot interaction: Reviewing the h in human-robot interaction,” ACM Transactions on Human-Robot Interaction, vol. 12, no. 3, pp. 1–28, 2023.
- M. B. Brewer, “Reducing prejudice through cross-categorization: effects,” Reducing prejudice and discrimination, pp. 165–85, 2000.
- M. Desai, M. Medvedev, M. Vázquez, S. McSheehy, S. Gadea-Omelchenko, C. Bruggeman, A. Steinfeld, and H. Yanco, “Effects of changing reliability on trust of robot systems,” in 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2012, pp. 73–80.
- E. Cha, A. D. Dragan, and S. S. Srinivasa, “Perceived robot capability,” in 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 2015, pp. 541–548.
- P. Khadpe, R. Krishna, L. Fei-Fei, J. T. Hancock, and M. S. Bernstein, “Conceptual metaphors impact perceptions of human-ai collaboration,” Proceedings of the ACM on Human-Computer Interaction, vol. 4, no. CSCW2, pp. 1–26, 2020.
- P.-H. Orefice, M. Ammi, M. Hafez, and A. Tapus, “Let’s handshake and i’ll know who you are: Gender and personality discrimination in human-human and human-robot handshaking interaction,” in 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids). IEEE, 2016, pp. 958–965.
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.