Analyzing Gender and Intersectionality in Social Robots

Can social robots be gendered? Do users gender robots even when designers intend to create gender-neutral devices (Nass & Moon, 2000)? Understanding how robots are gendered can help researchers, designers and users make conscious decisions about what features to look for in a robot and how robots and chatbots might enhance social equality, or at least not hinder it (see Gendering Social Robots).

1. Consider gender in user perspectives
How are users engaged in the product lifecycle, from planning to consumption?
  • a. Have you included a wide and representative variety of users of different genders/sexes, ethnicities, ages, socio-economic statuses, heights, (dis)abilities, body sizes, etc., and have you considered the intersection between these attributes? User configuration will depend on the product you envisage (Rohracher, 20005; see Facial Recognition).
  • b. Is your product designed to fit users with different body types, e.g. short women and tall men? Envisaged user height and weight may have sex implications for many technologies, such as exoskeletons (Odah et al., 2018; Søraa and Fosch-Villatonga, forthcoming).
  • d. Is there a risk of excluding certain users through the design of the technology, e.g. those with disabilities or of low socio-economic status (Tay et al., 2014)?
  • e. Do any robot features reinforce existing gender inequalities, gender norms or gender stereotypes (see Virtual Assistants)?
  • f. Do any robot features reinforce existing social roles (e.g. gender segregation in the workforce, such as men being associated with engineering and women with domestic technologies) (see Gendering Social Robots)?
2. Is your robot designed to promote social equality?
Roboticists have the opportunity to challenge gender stereotypes in ways that can lead users to rethink gender norms: 3. What features elicit gender attribution in robots?
These practices could lead to gendered robots:
  • a. Choosing a male or female name to label the robot (Kraus et al., 2018; Crowell et al., 2009; Alexander et al., 2014; Kuchenbrandt et al., 2014; Reich-Stiebert & Eyssel, 2017; Tay et al., 2014).
  • b. Colour-coding the robot (Jung et al., 2016; Powers et al., 2005).
  • c. Manipulating visual indicators of gender (for example, face, hairstyle or lip color) (Powers et al., 2005; Eyssel & Hegel, 2012).
  • d. Using a low-pitched or high-pitched voice (Kraus et al., 2018; Crowell et al., 2009; Alexander et al., 2014; Kuchenbrandt et al., 2014; Reich-Stiebert & Eyssel, 2017; Tay et al., 2014; Powers et al., 2005; Siegel et al., 2009; Eyssel et al., 2012).
  • e. Designing a gendered personality (Kraus et al., 2018; Kittmann et al., 2015).
  • f. Deploying robots in gender-stereotypical domains, such as a male-voiced robot for security and a female-voiced robot in a healthcare role (Eyssel & Hegel, 2012).
Other aspects which may gender a robot, such as movement or gesture, still require empirical research (Alesich & Rigby, 2017; Søraa, 2017).

4. Consider gender in human-machine interaction
Test the interaction between human participants’ sex/gender and robot “gender". Test with different gendered parameters, if relevant (Jung et al., 2016; Powers et al., 2005; Nomura 2017).
  • a. How do the sex and gender of humans and robots affect human-robot interaction, emotion, cognition and behavior? How might these interactions be gendered?
5. Consider the robot's socio-cultural context
When designing a robot for a global audience, consider socially and culturally specific aspects of sex/gender (and robots) as these play out in different parts of the world.
  • a. Consider the impact of the social context or domain (healthcare, education, security, home, etc.) within which the human-robot interaction takes place (Bartneck et al., 2007).
  • b. Consider the culture, region and country in which the robot will be implemented. What are the gender norms in that region? What assumptions might the culture have regarding robot-human interaction (Fraune et al., 2015)?
  • c. Consider the social relations and intersectionality of the individuals in the contexts in which the robots are placed (e.g. would robots inherit racial bias, see: Bartneck et al., 2018).

Related Case Studies

Gendering Social Robots
Haptic Touch
Virtual Assistants
Facial Recognition
Machine Translation

Works Cited

Alesich, S., & Rigby, M. (2017). Gendered robots: implications for our humanoid future. IEEE Technology and Society Magazine, 36(2), 50-59.

Alexander, E., Bank, C., Yang, J. J., Hayes, B. & Scassellati, B. (2014). Asking for help from a gendered robot. Proceedings of the Annual Meeting of the Cognitive Science Society, 36, 2333–2338.

Bartneck, C., Suzuki, T., Kanda, T., & Nomura, T. (2007). The influence of people’s culture and prior experiences with Aibo on their attitude towards robots. AI & Society, 21(1-2), 217-230.

Bartneck, C., Yogeeswaran, K., Ser, Q. M., Woodward, G., Sparrow, R., Wang, S., & Eyssel, F. (2018). Robots and racism. In Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 196-204). ACM.

Beraldo, G., Di Battista, S., Badaloni, S., Menegatti, E., & Pivetti, M. (2018). Sex differences in expectations and perception of a social robot. In 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (pp. 38-43). IEEE.

Crowell, C. R, Scheutz, M., Schermerhorn, P., & Villano, M. (2009). Gendered voice and robot entities: perceptions and reactions of male and female subjects. In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3735–3741). Piscataway, NJ: IEEE Press.

Eyssel, F., Kuchenbrandt, D., Hegel, F. & de Ruiter, L. (2012). Activating elicited agent knowledge: how robot and user features shape the perception of social robots. In Proceedings of the 21st IEEE international Symposium on Robot and Human Interactive Communication (pp. 851–857). IEEE.

Eyssel, F., & Hegel, F. (2012). (S)he's got the look: gender stereotyping of robots. Journal of Applied Social Psychology, 42(9), 2213-2230.

Fraune, M., Kawakami, S., Sabanovic, S., de Silva, R., & Okada, M. (2015). Three’s company, or a crowd? The effects of robot number and behavior on HRI in Japan and the USA. In L. E. Kavraki, D. Hsu, & J. Buchli (Eds.), Proceedings of Robotics: Science and Systems. doi:10.15607/RSS.2015.XI.033

Jung, E. H., Waddell, T. F. & Sundar, S. S. (2016). 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 (pp. 3107–3113). New York: ACM.

Kittmann, R., Fröhlich, T., Schäfer, J., Reiser, U., Weißhardt, F., & Haug, A. (2015). Let me introduce myself: I am Care-O-bot 4, a gentleman robot. In Mensch und computer 2015–proceedings. Stuttgart: De Gruyter Oldenbourg.

Kraus, M., Kraus, J., Baumann, M. & Minker, W. (2018). Effects of gender stereotypes on trust and likability in spoken human-robot interaction. In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo A. Moreno, J. Odijk, S. Piperidis, T. Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (pp. 112–118). European Language Resources Association.

Kuchenbrandt, D., Häring, M., Eichberg, J., Eyssel, F., & André, E. (2014). Keep an eye on the task! How gender typicality of tasks influence human–robot interactions. International Journal of Social Robotics, 6(3), 417-427.

Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81-103.

Nomura, T. (2017). Robots and gender. Gender and the Genome, 1(1), 18-26.

Odah, E., Abu-Qasmieh, I., Al Khateeb, N., Al Matalbeh, E., Qura’an, S., Mohammad, M. & Alqudah, A. M. (2018). Gender considerations in optimizing usability design of hand-tool by testing hand stress using sEMG signal analysis. Alexandria Engineering Journal 57(4), 2897-2901.

Powers, A., Kramer, A. D. I., Lim, S., Kuo, J., Lee, S., & Kiesler, S. B. (2005). Eliciting information from people with a gendered humanoid robot. In Proceedings of the 2005 IEEE International Workshop on Robots and Human Interactive Communication, ROMAN 2005 (pp. 158-163). IEEE.

Reich-Stiebert, N. & Eyssel, F. (2017). (Ir)relevance of gender? On the influence of gender stereotypes on learning with a robot. In B. Mutlu & M. Tscheligi (Eds.), Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 166–176). New York: ACM.

Rohracher, H. (2005). From passive consumers to active participants: the diverse roles of users in innovation processes. In User Involvement in Innovation Processes: Strategies and Limitations from a Socio-Technical Perspective (eds. Rohracher, H.) Munich: Profil.

Siegel, M., Breazeal, C. & Norton, M. I. (2009). Persuasive robotics: the influence of robot gender on human behavior. In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2563–2568). Piscataway, NJ: IEEE Press.

Søraa, R. A. Mechanical genders: how do humans gender robots? (2017) Gender, Technology and Development, 21(1-2), 99-115.

Søraa, R. A. and Fosch-Villaronga, E. (2020). Exoskeletons for all? Wearable robot technology through an intersectionality lens. Paladyn Journal of Behavioral Robotics, 11(1), 217-227.

Tay, B., Jung, Y., & Park, T. (2014). When stereotypes meet robots: the double-edge sword of robot gender and personality in human–robot interaction. Computers in Human Behavior, 38, 75-84.

 

 

double logo double logo double logo

 

TermsSite Map