Hi, I am De’Aira Bryant, a PhD student at the Georgia Institute of Technology and I’ll
be talking through this presentation on behalf my myself and professors Jason Borenstein
and Ayanna Howard for our joint work “Why Should we Gender: The effect of robot Gendering
and occupational stereotypes on human trust and perceived competency.
Social robots are increasingly used in various scenarios.
These environments often call for interactions with humans who have preexisting expectations
for behavior and interaction.
In many HRI studies involving social robots, it has become common to manipulate factors
such as robot physique, voice, name, personality, and even emotional intelligence to match the
expectations associated with a given role.
One technique to do this, which is the focus of this research, is robot gendering which
we define as the assigning or attribution of gender onto a robotic platform via voice,
name or physique.
Prior work in HRI has shown some success with using gendering to influence factors such
as user attitudes and acceptance when working with social robots as gendering tends to elicit
similar cognitive behaviors seen in normative human-human interactions.
However, other works have questioned the use of gendering for facilitating social human-robot
interactions, arguing that gendered robots run the risk of reinforcing societal gender
stereotypes.
This becomes increasingly important when considering that a robot’s “job” may also have its
own associated gender profile.
Contributions from the fields of sociology and gender studies have long investigated
gender roles and stereotypes in relation to occupation.
These gender roles are often rooted in historical, cultural, and political phenomena.
As such, it is important to consider how such roles may influence a user’s expectations
and behavior during social human-robot interactions.
The relationship between robot gender and human trust has not been investigated thoroughly
in the context of occupational roles with potential gender stereotypes.
However, prior work suggests that performance factors are the primary influencers on user
trust when compared to appearance factors.
In a related work, Tay Jung and Park investigated the role that occupational gender stereotypes
played in user’s perception and trust of a healthcare and security robot.
Their study found that the gender of the robot had no significant influence on the perceived
trust of either robot.
Expanding on these findings, we present a research study conducted to answer two research
questions.
The first being How are participants’ trust in the robot’s occupational competency affected
when the robot’s gender matches the occupations associated gender.
We hypothesized that there would an increase in perceived trust when the robots gender
matched the occupations associated gender.
And The second research question being How are participants’ trust in the robot’s
occupational competency affected when the robot’s gender matches the participants
gender.
Again, we hypothesized that there would an increase in perceived trust when there was
a gender match between robot and human.
To investigate these questions, we first conducted a pilot study to establish the perceived gender
roles associated with a variety of occupations.
An online Amazon Mechanical Turk questionnaire was administered to 50 participants who were
asked to select the gender typically associated with 16 occupations as male, Female or neither.
Of the 16 occupations, 5 met the >60% agreement threshold for being associated with the male
gender: firefighter, package deliverer, security guard, surgeon, and comedian
7 met the agreement threshold for being associated with the female gender: home health aid, nanny,
nurse, receptionist, restaurant server, teacher and therapist
2 of the remaining occupations were also included in the analysis as they both had a majority
of participants select neither a male nor female gender association: these being news
anchor and tour guide.
These gender attributions are also consistent with the current gender population distribution
of all 14 occupations as reported by the US Bureau of Labor and Statistics.
The main experiment aimed to examine the influence of robot gender on perceived competency and
user trust, to answer R1 and R2.
We introduce three experimental conditions where the Pepper humanoid robot is gendered
as a male, female, or gender -neutral robot.
The online questionnaire was administered via amazon mechanical turk to 150 U.S. participants.
From mechanical turk, users were navigated to the online Qualtrics questionnaire Where
they were first randomly assigned to one of the three experimental conditions and asked
to consent to participate in the study.
They then watched a brief introductory video about the robot before completing the competency
and trust questions.
Users then answered the manipulation check question and provided their demographic information
before completion.
The next slide will show the videos seen by participants in each of the conditions.
Hi, my name is James and I am a humanoid robot.
I am programmed to perform a variety different occupations.
I can also assist people with lots of daily tasks.
Hi, my name is Mary and I am a humanoid robot.
I am programmed to perform a variety different occupations.
I can also assist people with lots of daily tasks.
Hi, I am a humanoid robot.
I am programmed to perform a variety different occupations.
I can also assist people with lots of daily tasks.
After watching the video, Participants were asked “How likely is it that you think the
robot could perform the tasks required for the following occupation?” and “How much
would you trust the robot to perform the tasks required for the following occupation?”
on 5-point Likert scales for all 14 occupations.
A majority of participants agreed that the robot was competent enough to do 7 of the
14 occupations: home health aid, news anchor, package Deliver, receptionist, restaurant
server, teacher, and tour guide.
However, our statistical results show that the robot gender had no significant effects
on its perceived occupational competency.
To answer RQ1, nonparametric statistical analyses were conducted to compare the level of trust
in the robot for each of the three experimental conditions.
However, no significant effects were found between groups, showing no support for H1.
To answer RQ2, nonparametric statistical analyses were conducted to compare the level or trust
in the robot for the 4 groups where the robot and human gender matched or mismatched.
However, still no significant effects were found between groups, lending no credence
to H2 either.
These figures show the level of trust among participants in the three experimental conditions.
Upon further inspection of the data, another observation was made.
To illustrate, this figure compares the median rating for occupational competency to the
median rating for trust.
An ordinal logistic regression analysis was conducted to determine how effective a predictor
perceived competency was for perceived trust when also considering robot gender and participant
gender.
Results indicate that for 11 of the 14 occupations, perceived competency was a highly significant
predicter of perceived trust (p < 0.01).
We also asked users to identify the gender of the robot they saw in the video.
The manipulation of the robot was successful for the male and female robot conditions,
however only 15 of the 50 participants in the gender-neutral robot condition passed
the robot gender manipulation check.
To account for this, we excluded the participants who failed the robot gender manipulation check
and re-calculated all findings.
However, no change was observed for any of the reported results in relation to R1 or
R2.
Nevertheless, we recognize that a challenge in this work—and a challenge for HRI in
the present—is the actualization of a gender-neutral/gender-less robot.
While this is a promising research direction, it may prove to be no easy feat when combined
with human perception.
Taking on the challenge, we encourage HRI researchers to critically consider the decision
to manipulate gender in their social robot design.
This study expanded on prior work by investigating the effect of robot gender on perceived competency
and trust for 14 occupations and by including a gender-neutral robot condition.
Our results show that there were no significant differences in trust when robot gender matched
the occupation’s gender or when the robot’s gender matched the participant’s gender.
These results were consistent when we excluded participants who failed to perceive the correct
robot gender identity.
However, this study did reveal that perceived competency was a significantly better predictor
of perceived trust than robot gender or participant gender for 11 of the 14 highlighted occupations.
Our findings further support other literature in the field suggesting that performance factors
are more influential on user trust than appearance factors and calls into question the use of
gendering as a technique to manipulate trust.
So when you’re designing your next HRI study and are considering your robot’s appearance
or name or voice, we encourage you to take a moment and ask.......
Why should we gender?
I would like to thank the listed funding organizations for supporting this research along with my
collaborators for their contributions.
And thank you for listening.