What happens when robots are assigned ethnicities?
Tailored-bots may "help bridge the knowledge gap between humans in certain ethnicities.”
by Julianne Tveten
In 2010, a group of students and faculty members at Carnegie Mellon
University in Doha, Qatar, introduced their campus to Hala, the latest
in a line of what the school termed “roboceptionists.” Consisting of a
truncated torso and an LCD screen featuring a blue-skinned female CGI
head, Hala was designed to provide students and visitors with
instructions, directions, and anecdotes in either formal Arabic or
American English.
In addition to educating visitors about Qatar, Hala’s purpose was to
explore human-robot interaction (HRI) in a multicultural setting. The
population of Doha is a demographic mosaic; the city is primarily
inhabited by expatriates from all over the world (most of whom speak
Arabic and/or English). Because of this relative diversity, Hala
interacted with visitors from a slew of countries, using features like
speech recognition and the ability to perceive users’ facial expressions
to conduct, in Carnegie Mellon's words, “culturally appropriate”
exchanges.
Among the school’s robotics department, Hala’s development sparked a
flame of inquiry. If a robot could read different linguistic and visual
cues, could its communicative abilities improve? What might that mean
for the future of HRI?
In 2013, a group of doctoral students at Carnegie Mellon’s Pittsburgh
and Doha campuses explored these questions by publishing one of the
first multicultural HRI studies. Their hypothesis: if humans can relate
to a robot on the basis of culture, they’ll respond more positively to
the bot and recall interactions more thoroughly and accurately.
Hala at work at Carnegie Mellon Qatar.
Carnegie Mellon University Qatar
“If you have a robot that’s interactive, there’s reason to expect
that a person will want to bond with the robot subconsciously,” Maxim
Makatchev, one of the study’s researchers, told Ars. “If a robot makes
bonding easier, the interaction will potentially be more successful, and
expressing social cues makes it easier to bond with the robot.”
A bit of background is in order. Plenty of bots have been built to
resemble humans’ varying appearances and behaviors, but they’ve
reflected only their creators’ cultures. Persian scientist Ibn Sina
developed an Arabic-speaking humanoid robot; the American-built BINA48,
the robot inspired by biotech executive Martine Rothblatt’s wife Bina
Aspen Rothblatt, and Japanese engineer Hiroshi Ishiguro’s geminoids—which
stand, perhaps, as the poster children for anthropomorphic robots—are
hyperrealistic likenesses of their (mostly Japanese) inspirations.
It's BINA48.
Terasem
“If you look at the human-like robots, for example, in Japan or in
Europe, [their engineers] make them look like the people around them,”
said Makatchev. “The Japanese robots are usually Asian, and the European
robots are usually Caucasian, presumably because that's what the
researchers look like.”
Since there’s little precedent for multicultural HRI, a colossal
problem looms for any scientist who aims to broach the subject:
stereotypes. Outfitting a nonhuman entity with an ethnic identity can
easily fall prey to prejudice and reductivism, particularly if
developers are exploring interactions with cultures with which they
aren’t intimately familiar.
With this in mind, the researchers decided to test ethnic attribution
using verbal and nonverbal cues that could be measured objectively:
greetings, gaze patterns, politeness, response to mistakes, and response
to disagreement. They divided four anthropomorphic robots that were
based on Hala’s software architectures into two linguistic categories:
native speakers of Arabic who spoke English as a second language
(abbreviated as Ar) and native speakers of American English (AmE). To
gain insight into these behavioral cues, the researchers monitored
conversations between speakers of both languages and used crowdsourced
surveys to gauge mannerisms for native speakers of each language.
Each robot in the study had one of four unique female faces. (When
asked why they were female, Makatchev said the Hala prototype offered a
stronger foundation for designing a female robot than a male one.) Face 1
(dark hair, olive skin) was designed to portray a native Arabic speaker
and Face 2 (blonde hair, fair skin) a white, American-born English
speaker. Faces 3 and 4, which appeared more overtly mechanical, were
used as controls to test the efficacy of the behavioral cues without the
signal of appearance.
Maxim Makatchev
Seventeen human Ar and AmE speakers held conversations with each of
the four robots (whose responses were selected by a human operator) in
which the humans typed requests for directions to various destinations.
In each interaction, both parties spoke English, and researchers
randomly and equally assigned behavioral cues to the bots. Those with Ar
behavioral cues, for instance, greeted users with “Yes, ma’am/sir,” had
a moving gaze, and used excuses to explain mistakes), while those with
AmE cues said, “Hi,” kept their eyes on the user, and made a “lower lip
pull”—an expression one might make while saying, “Eesh!”—in response to
mistakes.
Perhaps unsurprisingly, the study provided virtually no support for
the cultural-bonding hypothesis. Few combinations of behaviors and faces
showed differences in perception between the AmE and Ar participant
populations; both groups viewed AmE bots as more animated, and both
rated Face 4—the least humanoid—most likeable. Similarly, AmE humans
didn’t view greetings like “Yes, ma’am/sir” as AmE behavior but found
them likeable.
What’s more, communicative efficacy didn’t prove ethnically
sensitive. When asked to recall robots’ answers to their questions from
particular conversations, Ar and AmE participants showed no significant
difference in recollection when discussing responses from either robotic
group.
To parse the value of this conclusion, it's imperative to consider
the study's limitations. The robots’ human likeness was minimal, and
each had the same voice. Users typed their questions and responses,
compromising their abilities to look forward and examine the robots'
behaviors. Additionally, the study accounted for only two ethnic
identities and used a relatively small sample size. How much, then,
might results shift if these factors are changed?
Too little subsequent work exists to answer that question, but the
Carnegie Mellon study may provide a foundation on which to explore
robots’ roles in multicultural coexistence. If the conclusions of this
study bear any validity, is it possible that prejudices humans harbor
against one another don’t translate to their interactions with non-human
entities, even when those things resemble humans? Can interactions with
robots enlighten people about cultures they don’t know and thus the
interactions they have with other humans?
A participant interacts with a robot bearing Face 1.
Maxim Makatchev
Makatchev believes there’s potential. “Robots that express certain
ethnicities can help bridge the knowledge gap between humans in certain
ethnicities,” he said. “In Qatar, the local Qataris never have an equal
power relationship with, for example, Southeast Asians. Most Southeast
Asians come there as service workers or construction workers. There are
no conditions for positive contact between Southeast Asians and Qataris.
Maybe a technology like robots could facilitate positive contact by
presenting themselves as an avatar of a certain ethnicity which does not
have this power imbalance.”
It's possible, but considering the current state of HRI, the
interpretation seems rosy. Humanoid, idiosyncratic robots are relatively
new, and humans might not take them seriously as cultural agents.
Furthermore, in their current states, robots are time-consuming and
expensive to build, and they’re inaccessible to most people. Given their
distance from the human biological and social canon and the resources
needed to create them, it's reasonable to doubt that they can
realistically serve as cultural mediators—or that incorporating ethnic
identities into robots can truly have any positive effect at all. (This
rings especially true in the wake of Tay, Microsoft’s AI simulation of a
millennial that, after a few hours of exchanges with 4Chan-caliber
Twitter trolls, transformed into a vociferous champion of Hitler.)
Still, Makatchev said, “Whether we want it or not, some people will
continue to [experiment with ethnic attribution in robots]. You can’t
restrict these kinds of studies or these kinds of industrial designs.
Even if there’s no academic research, it doesn’t mean that industry will
not go that way.”
If ethnic attribution in robots is as inevitable as Makatchev says,
there's reason to be concerned. Perhaps the largest issue is one of
multicultural presence within the science and tech communities
themselves. The diversity problem still looms large; though Carnegie
Mellon’s experiments took advantage of ethnically varied settings, this
isn't reflective of most environments in which technology is developed.
It's potentially dangerous to create culturally representative
technology in demographically homogeneous circles, where engineers might
not enjoy the awareness provided by a broad multicultural scope.
If robots are, to an extent, reflections of their makers, it’s only
when those engineers benefit from exposure to a wide range of
perspectives, open communication, and heightened consciousness and
empathy that any sort of constructive marriage of robots and ethnicity
can happen. Only time will tell if it will. Julianne Tveten is a journalist specializing in the
sociopolitical currents of technology. At Ars, she's previously written
about CSS colors, connectivity issues for American Indian populations, and open source voting. Listing image by Maxim Makatchev
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