Exploring Gender-Expansive Categorization Options for Robots
Katie Seaborn

Tokyo Institute of Technology
seaborn.k.aa@m.titech.ac.jp

Peter Pennefather

gDial, inc.
p.pennefather@gmail.com

ABSTRACT
Gender is increasingly being explored as a social characteristic ascribed to robots by people. Yet, research involving social robots that
may be gendered tends not to address gender perceptions, such as
through pilot studies or manipulation checks. Moreover, researchthat does address gender perceptions has been limited by a relianceon the human gender binary model of feminine and masculine, prescriptive response options, and/or researcher assumptions and/or
ascriptions of participant gendering. In response, we conducted
an online pilot categorization study (n=55) wherein we provided
gender-expansive response options for rating four robots ranging
across four levels of anthropomorphism. Findings indicate that people gender robots in diverse ways, and not necessarily in relation to
the gender binary. Additionally, less anthropomorphic robots andthe childlike humanoid robot were deemed masculine, while theiconic robot was deemed gender neutral, fluid, and/or ambiguous.
We discuss implications for future work on all humanoid robots.

CCS CONCEPTS• Human-centered computing → Human computer interaction(HCI); • Social and professional topics → User characteristics;
Gender.

KEYWORDS
Robots, Gender, Robot gender, Gender-expansive, Anthropomorphism, Age
ACM Reference Format:
Katie Seaborn, Peter Pennefather, and Haruki Kotani. 2022. Exploring
Gender-Expansive Categorization Options for Robots. In CHI Conference on
Human Factors in Computing Systems Extended Abstracts (CHI ’22 Extended
Abstracts), April 29–May 05, 2022, New Orleans, LA, USA. ACM, New York,NY, USA, 6 pages. https://doi.org/10.1145/3491101.3519646

1

INTRODUCTION

Gender is a social characteristic constructed by people and applied
to other agents, especially human-like ones. Indeed, there may be
no category as fundamental to human perception as gender [22].
Although the act of gendering is a cognitive process, it has material
consequences. Decades of research has shown that people tend tounthinkingly ascribe gender to computer-based agents and react
in stereotyped ways to this gendering [21, 23–25]. In particular,
robots that interact with people have emerged as a target of study.
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CHI ’22 Extended Abstracts, April 29–May 05, 2022, New Orleans, LA, USA© 2022 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-9156-6/22/04.
https://doi.org/10.1145/3491101.3519646

Haruki Kotani

Tokyo Institute of Technology
kotani.h.ab@m.titech.ac.jp

Efforts in human-robot interaction (HRI) have indicated that theperceived gender of the robot [3, 4, 6, 8, 10, 13, 19, 27, 29], the genderof the participant [8, 9, 18, 30, 33], and the gendering of the robot’s
embodiment in terms of role, task, and/or environment [5, 20, 31]
can play a significant role.
Yet, research on gendering robots remains nascent. Most workso far has been theoretically and methodologically limited in a few
key ways. Gender tends to be undertheorized and taken for granted
in line with human models of gender [14, 34, 37], almost exclusively
positioned within the gender binary of masculine and feminine.
Manipulation checks of gender are underutilized [34]. Researchers
may not consider gender, even if gender cues are present in the robot[1]. They may prescribe a limited set of gender options for robots
and/or participants or rely on a limited set of gender options whenevaluating participant data [1, 34, 39], typically two: “male/man”
and “female/woman.” As one critical review using the case studyof social robot Pepper found, researchers’ models of gender may
also affect how participants themselves view the robot’s gender aswell as how their data is analyzed [34]. What is needed are more
rigorous and theoretically grounded explorations of robot genderthat are sensitive to inclusive alternatives.
As a first step, we conducted an online pilot study in which
participants categorized robots that had varying degrees of anthropomorphism using a gender-expansive set of response options. Our
research question was: How does a gender-expansive framing affectparticipant categorization of robots? Our contributions are threefold.
First, a methodological approach to assessing the gender of social
agents using a gender-expansive framework that can be scaled orapplied as a manipulation check. Second, empirical findings thatshow variability across participants when it comes to robot gendering. Third, evidence that anthropomorphism and perceived ageof the robot affect how the robot is gendered. We end with a callfor the use of gender-expansive response options and greater rigor
when approaching gender as a variable of study in social agent
research. While the results are preliminary and limited to a passive
activity, they illustrate the pervasive and multifaceted nature of thegendering that occurs in—and may be set by—first impressions.

2

THEORETICAL BACKGROUND

Anthropomorphism is the tendency for people to ascribe humancharacteristics onto non-human objects, agents, and environments[12]. A wealth of research stretching back decades has pointed
to this phenomenon occurring with respect to robots and otheragents [21, 23–25]. The human tendency to anthropomorphize isthought to be reflexive and largely automatic, where anthropomorphic ascriptions emerge as first impressions [16]. Gender is one suchascription. Gender refers to the social categories by which people
organize themselves [17, 42]. Gender is typically constructed along
a continuum of masculinity and femininity, a model referred to as
the gender binary [17]. People may move between the poles fluidly,

CHI ’22 Extended Abstracts, April 29–May 05, 2022, New Orleans, LA, USA

occupy an ambiguous spot in the middle, or reject the binary entirely, i.e., non-binary genders. Sex is often positioned against (andconflated with) gender; by sex, we refer to the biological configurations of the body, including gonads, genitals, hormones, secondary
sex characteristics like body hair, and so on [17]. Some have argued
that it is difficult to distinguish between gender and sex, at least
given the current state of research and societies, arguing for theuse of “gender/sex” to account for this [17]. Of course, this may not
apply to robots and other mechanical entities, given that machines
have no basis in biology. Notably, gender is multi-functional as aprecept. It refers to self-identity in terms of inner alignment withgender concepts as well as outward self-expression through bodily
accoutrements, i.e., clothing, speech style, stance, etc. It also refers
to roles and expectations in society. It can also be ascribed by others,
not only to other people but also animals, objects, environments,
and non-humans, such as robots.
Gender is not a new concept in research on people’s interpretations of robots and other computer-based agents. Moreover, theconsensus is that people do gender socially embodied artificial
agents, and often in stereotyped ways that align with models ofhuman gender [5, 13, 21, 24, 28, 29]. For example, Eyssel and Hegel[13] found that people rated the masculine robot as more suitable
for masculine tasks, such as transporting goods, over feminine
tasks, such as caregiving. However, most research has used limited
response options for gendering (or not gendering) robots, in particular based on the human binary model, with perhaps a "no gender"
option [34, 35]. Yet, as discussed, human gender models are morediverse than the binary. Moreover, robots may not be perceived inline with human gender models, i.e., they may be perceived as having mechanical or robotic genders, or other genders [37]. Finally,
research on low and non-anthropomorphic agents, objects, andeven shapes and symbols, including birds, fruits, clouds, triangles,
and circles, suggests that people will anthropomorphize stimuli inline with gender stereotypes about agency and communality, evenin the absence of clear human cues [7, 22]. Thus, we may expect
that, if given gender expansive options:
H1: People will gender robots in diverse ways regardless of therobot’s anthropomorphism.
Nevertheless, degree of anthropomorphism may play a role. A
face-value judgment of the above literature would lead us to conclude that a robot with highly anthropomorphic cues would stimulate gendering. Consider Ishiguro’s Geminoid, a robot modeled after
Ishiguro himself through full-body mimicry of his own features,from skin to clothes to hair [26], to the extent that people may not
be able to tell Ishiguro apart from his Geminoid. As such, we canhypothesize that:
H2a: If the degree of the robot’s anthropomorphism is high,people will perceive the robot to have a gender.
At the same time, the absence or scarcity of human gender cues
could affect gendering. Søraa [37] conceptualized the notion of“mechanical genders” for robots and other agentic machines. Mechanical genders, which may also be called “robot genders,” areparticular to robots: a gender construction created by people toorganize non-human agents. Neutral genders and the absence ofgendering may also occur, but almost no research has exploredthis empirically in HRI or other domains in which artificial agentsare studied, including HAI and CHI [35]. Alternatively, due to a

Katie Seaborn et al.

discovery of male-as-default biases in HRI research [4] and the link
between perceptions of agency and masculinity [22], people may
perceive low anthropomorphic robots as masculine, even if not
humanlike. Moreover, there may be participant-specific cultural,
language, or individual cues that bias perception of gender. As such,
we hypothesize that:
H2b: If the degree of the robot’s anthropomorphism is low, people will perceive the robot to have a neutral or non-humanoid
gender (e.g., gender neutral, robotic gender, no gender).
Another anthropomorphic characteristic that can intersect withgender is age. A classic study, for instance, showed that one infant given different gender labels was gendered by different adults
in stereotyped ways [36]. Notably, the infant’s actual gender wastreated neutrally, e.g., the infant was considered genderless unlessand until ascribed gender markers, such as a name. Indeed, infants
and young children may colloquially be considered, or taken for
granted, as “gender neutral” or “genderless.” Findings for robots
are few and mixed and may have been affected by methodologicallimitations related to the response options provided to participants.
For instance, Sandygulova and O’Hare [32] provided only two options in line with the gender binary when asking children about
how they would ascribe the gender and age of a robot’s voice. With
gender-expansive options, the results may change. As such, wehypothesize that:
H3: If the robot is perceived as young (baby, child), it will also
be perceived as genderless.

3

METHODS

We conducted an online within-subjects categorization study, where
all participants categorized all robots, which were presented in arandom order. This protocol was registered on OSF1 before data
analysis on Dec. 7th , 2021.

3.1

Participants

Fifty-five people located in the US were recruited through Mechanical Turk. Only those with Masters status and a HIT approval rateof over 95% were recruited. For an additional level of quality assurance, we used the procedure by Nicoletti2 , whereby we generated
a set of random codes that respondents inputted on Mechanical
Turk after completing the study on SurveyMonkey. Most people
were aged 35 to 44 (n=21), with the rest aged 25 to 34 (n=11), 45
to 54 (n=11), 55 to 64 (n=7) and 65+ (n=5). In terms of gender, 34
identified as men/masculine and 21 identified as women/feminine;
no one withheld their gender or identified as another gender, e.g.,transgender, non-binary, or another gender.

3.2

Procedure

We used SurveyMonkey to facilitate the study. Participants were
provided with a link. After giving consent on the first page, they
were presented with an overview image of the four robots arranged
in a circle. We did this to help generate a baseline impression of therange of robots to be categorized and thus account for issues arisingfrom relative assessments of each individual robot. Participants
1 https://osf.io/2cuqv

2 http://nicholasnicoletti.com/survey-monkey-and-mechanical-turk-theverification-codeExploring Gender-Expansive Categorization Options for Robots

were then presented with the series four robots in a random order,
one per page. They were asked to carefully categorize each robot.
Demographics were captured on the last page.

3.3

Materials

Four US-made robots were sourced from the IEEE Robots3 database.
US-made robots were chosen to match the US participant pool andthus avoid “made-in” or other effects of the robots’ designs [38]. As
per Spatola et al. [38], we used the Duffy taxonomy of anthropomorphism [11] to select robots representing four discrete levels ofanthropomorphism: industrial, mechanical humanoid, iconic humanoid, and humanoid; figure available on OSF4 . Like Spatola et al.
[38], we cropped the images to a profile view for consistency. The
robots were Cody5 , CHARLI6 , Bandit7 , and Diego-san8 .

3.4

Measures3.4.1 Perceived Gender. A custom scale based on Seaborn andFrank [34] was used to capture participants’ perceptions of robot sex/gender. A 5-point gender binary scale, with feminine andmasculine poles plus a neutral centre, was combined with two nominal options: “robotic-specific gender” (however perceived by each
respondent) and “it does not have any sex/gender.”3.4.2 Perceived Age. A custom 5-point ordinal scale with baby (∼2),
child (3-12), teenager (13-19), adult (20-64), and older adult (65-)
options was provided, plus an “ageless” option.
3.4.3 Perceived Anthropomorphism. The 5-point semantic differential anthropomorphism subscale from the Godspeed instrument
was used [2]. The Godspeed is a well-validated scale that has beentranslated into many languages and is often used in HRI research.
3.4.4 Demographics. Participant demographics, reported above,were collected at the end of the survey. Age response options were
provided in 5-year increments starting from 18 until 75, for which
the option was “75 or older.” Sex/gender [17] response options were
woman/feminine, neutral/non-binary/gender fluid, man/masculine,
and prefer not to say/another gender.

3.5

Data Analysis

Perceived gender of the robot was separated by ordinal binary
continuum and nominal categories. Descriptive statistics were generated for each type of data, i.e., averages for the continuums andcounts for the nominal data. The items in the perceived anthropomorphism (Godspeed) scale were averaged together to create
one score. We used inferential statistics to answer the hypotheses,
mostly nonparametrics given the largely nominal data.

4

RESULTS

CHI ’22 Extended Abstracts, April 29–May 05, 2022, New Orleans, LA, USA

4.1

Gendering Robots in Diverse Ways (H1)

Each robot was categorized using the range of gender-expansive
options made available to participants; see Table 1. No one robot was
ascribed only one gender option. We can thus accept the hypothesisthat people tend to gender robots in diverse ways regardless of therobot’s anthropomorphism.

4.2

Anthropomorphism in Perceptions of
Robot Gender (H2a and H2b)

The Godspeed anthropomorphism subscale indicated a trend inperceived anthropomorphism: for Cody, a mean of 1.2 (SD=0.3,
MD=1, IQR=1.3), for CHARLI, a mean of 1.6 (SD=1.6, MD=1.3,
IQR=3.8), for Bandit, a mean of 2.2 (SD=0.8, MD=2, IQR=2.4), andfor Diego-san, a mean of 3.5 (SD=1, MD=3.5, IQR=2.3). A significant correlation was found between these scores and our Duffy
anthropomorphism categorizations, confirming our assignments,
rs (53) = .714, p = < .001.
A significant difference was also found in gender counts acrossthe Duffy anthropomorphism levels, X2 (6, 55) = 56.41, p < .001. This
indicates that gender perceptions differed by degree of anthropomorphism. The counts, for both binary and robotic genders, show
an apparent trend towards an inverse relationship between degreeof anthropomorphism and gender. Specifically, less anthropomorphic robots tended to be perceived as having a robotic gender, andvice versa for more anthropomorphic robots. Less anthropomorphic
robots were also perceived as being more masculine. For instance,
while the industrial robot, Cody, was generally ascribed a robot
gender, it was also placed in the gender binary at the masculine
end. However, the reverse was not true, pointing to a masculine
bias. In particular, the humanoid robot, Diego-san, tended to be
categorized within the gender binary at the masculine end, eventhough it is childlike with no gender markers. “Diego” is masculine,
but this name was not revealed to participants and is not common
knowledge. Still, the iconic robot (Bandit) was generally assigned
a gender, one within the binary and close to the neutral centre(M=3.1, SD=1.1). This suggests that it may generally be perceived
as a gender neutral or gender ambiguous robot.
Overall, we can accept the hypothesis that people tended to perceive robots with high anthropomorphism as gendered. However,
we can only partially accept the hypothesis that people tended toperceive robots with low anthropomorphism as having a robotic
or neutral gender. Indeed, participant ascriptions of gender for all
robots indicate that these hypotheses are limited. As our analyses show, there is a complicated relationship between anthropomorphism and the gender binary. The results for the industrial
robot—the least anthropomorphic—indicate diverse binary gendering when it takes place. The results for the humanoid robot will befurther teased out in the next section.

We present the results by hypothesis below.

4.3

3 https://robots.ieee.org/robots

Ascriptions of age tended to follow the robot’s anthropomorphism
level, with age categories assigned to more anthropomorphic robots
and the “ageless” category assigned to less anthropomorphic robots;see Table 2. A significant difference was found in age category
counts across the Duffy anthropomorphism levels, X2 (6, 55) = 54.35,

4 https://osf.io/duxy45 https://robots.ieee.org/robots/cody

6 https://robots.ieee.org/robots/charli

7 https://robots.ieee.org/robots/bandit

8 https://robots.ieee.org/robots/diegosan

Age in Gender and Anthropomorphism(H3)

CHI ’22 Extended Abstracts, April 29–May 05, 2022, New Orleans, LA, USA

Katie Seaborn et al.

Table 1: Gender-expansive categorization of the robots
Robot

Anthro.

Gender Binary
Mean
St. Dev.

MedianIQR

Count

Robotic GenderCount

No Gender
Count

Cody
CHARLI
BanditDiego-sanInd.
Mech. Hu.
Ico. Hu.
Hu.

1.9
2.1
3.1
1.3

2
2
3
1

2
2
3
3

19
32
42
52

31
16
7
1

5
7
6
2

.9
.9
1.1
.6

Table 2: Age categorization of the robots
Robot

Anthro.

GenderAge
MeanSt. Dev.

MedianIQRCount

AgelessCount

Cody
CHARLI
BanditDiego-sanInd.
Mech. Hu.
Ico. Hu.
Hu.

Robot-Masculine
Robot-Masculine
Neutral-Ambiguous
Masculine

4.1
3.9
3.5
2.1

.4
.3
.8
.4

4
4
4
2

1
0
2
2

19
32
42
54

36
23
13
1

p < .001. As with gender, the count distributions suggest an inverse
relationship between perceived age and anthropomorphism.
Most robots (3 out of 4 or 75%) were assigned an “adult” age range(score of 3 or 4) and one was assigned a “child” age (Diego-san). For
this hypothesis, we focus on the childlike robot, Diego-san, which
was also categorized by ourselves and participants as humanoid,or highly anthropomorphic. As noted above, Diego-san tended tobe perceived by participants as masculine. Only one participant
perceived Diego-san as genderless. Based on these results, we must
reject the hypotheses that robots perceived as childlike are also
perceived as genderless.
After these unexpected results, we conducted an exploratory
analysis of the robot data. A significant relationship was foundbetween gender and age across the robots, Φ = .60, p < .001. The
descriptives provide insight about this relationship. Cody, the industrial robot, was generally ascribed as “ageless” and otherwise
categorized as adult in age. It also received the largest portion of“robot gender” ascriptions, while also tending towards masculine
when gendered in the binary. The next robot on the anthropomorphism scale, CHARLI, had similar results: robot-masculine, adult,
and otherwise roughly equal parts aged and ageless. Bandit wasalso generally perceived as adult in age, but neutral or ambiguous ingender. Overall, these results indicate that high anthropomorphism
is associated with perceptions of age (as opposed to agelessness), as
well as polar gender binary ascriptions, specifically the masculine
pole. In contrast, low anthropomorphism seems to be associatedwith agelessness and adult agedness, as well as a mix of robot,
masculine, and neutral or ambiguous gender attributions.

5

DISCUSSION

A gender-expansive approach to gendering robots appears to be not
only feasible but illuminating. Participants made use of the range of
gender response options made available to them—notably, not justthose within the gender binary. As expected, anthropomorphism
influenced gendering, but in slightly unexpected ways. Notably,corollaries between binary gender options and robot gender were

found. The more mechanical robots were ascribed Robot-Masculine
genders, while the iconic robot was deemed Robot-Neutral and thehumanoid robot was deemed Masculine (and not generally ascribeda robot gender). Gendering was not as predictable or consistent,
confirming recent calls for the use of gender diverse response options and alternative genders, especially robotic or mechanical ones
[34, 35, 37], as a critical component of research on robot gender/ing.
Consensus among participants about each robot’s gender was also
not achieved, as the standard deviations and interquartile ranges
indicate. In short, providing a set of gender-expansive response
options seems to influence participant categorization of robot gender, at least with this selection of robots. Future work will need to
expand the range of robots categorized to be sure.
Findings indicate that our gender-expansive framing may needfurther expansion. We provided a gender binary scale with a “neutral” (so-labelled) centre, as well as robotic gender and genderless
options, in line with Seaborn and Frank [34]. However, the results
for the iconic humanoid robot Bandit point to a limitation in thisframework. Bandit’s scores averaged to the middle of the genderbinary or inclined to a robotic gender ascription. We interpret thisto mean that Bandit was gender ambiguous. The response options
we provided were insufficient for drawing conclusions on how exactly it was gendered. It may be gender neutral (truly genderless orwithout gender), and/or gender blended (a mix of masculine andfeminine, and potentially other gender orientations), and/or genderambiguous (where people could not decide on how to frame its
gender) [35, 41]. Moreover, it is unclear why this robot, Bandit, wasan exception. Future work will need to explore the reasons directly,
such as through qualitative reports from participants explaining
their reasons or process of gendering. Determining whether the
robot is perceived as gender neutral, blended, and/or ambiguous
may also be revealed through studies placing it within different
gendered and non-gendered contexts of use or interactive activities.
These findings also point to potentially meaningful distinctions
between neutrality, ambiguity, and fluidity that future theoreticalwork can consider more deeply and empirical work with larger samples can pinpoint. This also highlights the limitations of relying onExploring Gender-Expansive Categorization Options for Robots

a frame comprised of a spectrum between two poles. Categorizing
complex phenomena is always tricky, as Alvin Stirling showed withrespect to complexity more generally [40]. As he argues, we should
avoid oversimplifying and “keep it complex” by embracing the risk,
ignorance, uncertainty, and ambiguity that characterizes knowledge production. We simply remain accountable for and moveable
on our decisions.
Exploring gender neutrality may be crucial in light of anothermajor finding: the male bias effect appears to have borne out, despite the gender-expansive frame provided to participants. Yet, asSeaborn and Frank [34] argue, gender is not static. Indeed, robots
and other artificial agents provide an opportunity for creative explorations of gender/ing, including shifting notions of gender for
people. Understanding when and how gendering occurs in the firststep. Limited and biased ascriptions of gender and any resulting
effects, especially stereotyped responses, may be malleable withthe right research tools, gender-conscious design, and sufficienttime. Alternatively, Martin and Slepian [22] suggest that we can“de-humanize gender.” Rather than “de-gendering” humans, andby extension de-gendering robots, the focus is on eliminating orcomplicating the idea that gender is only for humans. We can,for instance, first empirically demonstrate the absurdity of genderwhen applied to non-humans, and then spark questioning of theseabsurdities in relation to gendering people. This idea is premisedin a wealth of research linking certain anthropomorphic traits, especially the Big Two of agency and communality [15, 22], withthe gender binary. But this is a “chicken or the egg” situation: thedirection of influence is unknown. Moreover, this is the domain ofsocial construction, which conscious and concerted effort may beable to shape. The question is then philosophical: how do we wantto gender (or not)?
Age also appeared to intersect with gender, especially for thehumanoid robot, Diego-san, which was designed to be and generally
ascribed in this study as childlike. Unexpectedly, in considerationof human models of infants presumed to be genderless or at least
malleable in their gender presentation [36], Diego-san was almost
universally categorized within the binary, as masculine. The reason
why needs to be explored. As discussed, the male bias effect is
one possibility. Another is that there may have been unintentional
gender cues in Diego-san’s image. For instance, the green-andyellow checkered shirt or metal plate on the robot’s head may havebeen interpreted as masculine in the absence of other gender cues,
as per the male bias effect. Future research with other childlike
robots, including non-humanoid childlike robots, may be able totease out how much is the male bias effect, unintentional gendercues, or other latent causes.

5.1

Limitations

As a pilot study, our sample size was relatively small; it will be
increased in the full study to achieve greater statistical power. We
could not provide an input field for participants to suggest alternative gender options. This was due to a “total” question” limitationin the survey platform, on top of this research being folded into alarger project. The full study and other future work can explore
open-ended gender categorization. We evaluated only four robots,
but this will be expanded in the full study. Also, given the potential

CHI ’22 Extended Abstracts, April 29–May 05, 2022, New Orleans, LA, USAof cultural differences in gender/ing, future work will need to include participants outside of the US and robots made from othercountries. The full study will also involve more rigorous comparative inferential statistics, e.g., repeated-measures ANOVAs. Finally,
as with other categorization research, this methodology assesses
perceptions through a passive activity, which may be affected bylater interaction. Future work will need to explore the effects, if any,of first impressions while also including pre-tests of gendering.

6

CONCLUSION

When it comes to robots, gender is not a given, and neither is thegender binary. As our findings show, participants ascribed a range
of gender options to the robots under study. While a bias for thegender binary and in particular masculine ascriptions of robots was
discovered, other options, including robotic genders no gender ascriptions, were found. Moreover, the degree of anthropomorphismand perceived age of the robot appears to intersect. Considering
these findings, we argue for the use of gender-expansive response
options when categorizing perceptions of robot gender. We strongly
encourage those relying on perceptions of robot gender as a locus
of study to conduct pilot tests and/or manipulation checks to discover how participants gender and whether it matches expectations.
Future work can explore the extent to which these findings apply
to other robots. The results for age and anthropomorphism suggest
a path for explorations of gender neutrality, as well. Let us not takegender/ing for granted any longer.

ACKNOWLEDGMENTS
This research was financially supported by the university department of the first and last author.

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