from its AI experts Monday: Their

algorithms can now read fear on your
face, at a cost of $0.001 per image—or
less if you process more than 1 million

The news sparked interest because
Amazon is at the center of a political
tussle over the accuracy and
regulation of facial recognition.

Amazon sells a facial-recognition
service, part of a suite of image-
analysis features called Rekognition,
to customers that include police
departments. Another Rekognition
service tries to discern the gender of
faces in photos. The company said
Monday that the gender feature had
been improved—apparently a

response to research showing it was
much less accurate for people with
darker skin.

Rekognition has been assessing
emotions in faces along a sliding scale
for seven categories: “happy,” “sad,”
“angry,” “surprised,” “disgusted,”

“calm,” and “confused.” Fear, added
Monday, is the eighth.

Amazon isn't the first company to
offer developers access to algorithms
that claim to detect emotions.

Microsoft has had similar offerings
since 2015; its service looks for a
similar list of emotions, adding
“contempt” but deleting confusion.
Google has offered its own similar
service since 2016.

Amazon declined to detail how
customers are using emotion
recognition. Online documentation for
Rekognition warns that the service “is
not a determination of the person’s
internal emotional state and should
not be used in such a way." But on its
Rekognition website, Amazon, whose
ecommerce business has squeezed
brick-and-mortar retailers in part via
deep data on consumers, suggests that
stores could feed live images of

shoppers into its face-analysis tools to
track emotional and demographic
trends at different retail locations over

Even as Amazon, Google, and
Microsoft charge ahead with
algorithms that intuit feelings,
psychologists warn that trying to read
emotions from facial expressions is
fundamentally misguided.

A study published in February by UC
Berkeley researchers found that for a
person to accurately read someone
else’s emotions in a video requires
paying attention to not just their face
but also their body language and
surroundings. Software offered by
tech companies generally analyzes
each face in isolation.

Another study, published last month,
took more direct and devastating aim
at emotion-detection software.

Psychologists reviewed more than
1,000 published findings about facial
expressions and emotion and
concluded there was no evidence that
facial expressions reliably

communicate emotion on their own,
undermining the core assumption of
emotion-detection software.

“It is not possible to confidently infer
happiness from a smile, anger from a
scowl, or sadness from a frown, as
much of current technology tries to do
when applying what are mistakenly
believed to be the scientific facts,” the
authors wrote.

Rumman Chowdhury, who leads work
on responsible AI at Accenture, says
the situation is an example of the
industry not pausing to think through
the limitations of its technology. Even
if software could read faces
accurately, the idea of collapsing the
richness of human feeling into a
handful of categories for all people
and contexts doesn’t make much
sense, she says. But hype about the
power of AI has led many people
inside and outside the tech industry to
be overconfident about what
computers can do.
“To most programmers, as long as the
output is something reasonable and
the accuracy looks OK on some
measure, it’s considered to be fine,”
she says. Customers told that AI is
more powerful than ever are unlikely
to check the foundation of the claims,
Chowdhury says.
As with facial recognition, easier
access to emotion-recognition
algorithms seems to be causing the
technology to spread more widely,
including into law enforcement.
In July, Oxygen Forensics, which sells
software that the FBI and others use
to extract data from smartphones,
added facial recognition and emotion
detection to its product. Lee Reiber,
Oxygen’s chief operating officer, says
the features were added to help
investigators sort through the
hundreds or thousands of images that
often turn up during digital evidence
Officers can now search for a specific
face in an evidence trove, or cluster
images of the same person together.
They can also filter faces by race or
age group, and emotions such as “joy”
and “anger.” Reiber says visual tools
can help investigators do their work
more quickly, even if they are less
than perfect, and that the investigative
process means leads are always
checked multiple ways. “I want to
take as many pieces as possible and
put them together to paint a picture,”
he says

The number of commercial emotion-
detection programs is growing, but
they don’t appear to be very widely
used. Oxygen Forensics added facial
recognition and emotion detection
using software from Rank One, a
startup that has contracts with law
enforcement. But when WIRED
contacted Rank One CEO Brendan
Klare, he was unaware that Oxygen
Forensics had implemented emotion
detection in addition to facial

Klare says the emotion detector has so
far not proved popular. “The market’s
pretty limited at the moment, and it’s
not clear to us if it will ever pay off as
a feature,” he says. “It’s not something
that is that big right now.”

The changing focus of emotion-
recognition startup Affectiva
illustrates the challenge. The company
emerged in 2009 from an MIT project
trying to help people with autism
understand people around them.
won funding from investors that
include advertising giant WPP and
launched products to help marketers
measure audience reaction to
commercials and other content.
recently, the company has focused on
improving car safety, for example,
through technology to spot when
drivers are sleepy or angry. Affectiva
announced $26 million in funding
earlier this year, with auto parts
manufacturer Aptiv as lead investor.
The company declined to comment.
At least one big tech company appears
to have decided that emotion

recognition isn’t worth the effort. IBM
competes with Amazon and Microsoft
in cloud computing and facial
recognition but does not offer emotion
detection. An IBM spokesperson said
the company does not plan to offer
such a service.

Google does not offer facial
recognition, a decision it says resulted
from an internal ethical review raising
concerns that the technology could be
used to infringe privacy. But the
company’s AI cloud services will
detect and analyze faces in photos,
estimating age, gender, and four
emotions: joy, sorrow, anger, and

Google says its emotion-detection
features passed through the same
review process that nixed facial
recognition. The company has also
decided that it’s OK to apply the
technology to personal photos of its

Searching for “happiness,” “surprise,”
or “anger” in Google’s Photos app will
surface images with appropriate facial
expressions. It will also look for