A beauty contest was judged by AI and the robots didn't like dark skin
A beauty contest was judged by AI and the robots didn't
like dark skin
The first international beauty contest decided by an
algorithm has sparked controversy after the results revealed one glaring factor
linking the winners
One expert says
the results offer ‘the perfect illustration of the problem’ with machine bias.
By Sam Levin in San Francisco
Thursday 8 September 2016 18.42 EDT Last modified on
Thursday 8 September 2016 18.50 EDT
The first international beauty contest judged by
“machines” was supposed to use objective factors such as facial symmetry and
wrinkles to identify the most attractive contestants. After Beauty.AI launched
this year, roughly 6,000 people from more than 100 countries submitted photos
in the hopes that artificial intelligence, supported by complex algorithms,
would determine that their faces most closely resembled “human beauty”.
But when the results came in, the creators were dismayed
to see that there was a glaring factor linking the winners: the robots did not
like people with dark skin.
Out of 44 winners, nearly all were white, a handful were
Asian, and only one had dark skin. That’s despite the fact that, although the
majority of contestants were white, many people of color submitted photos,
including large groups from India and Africa.
The ensuing controversy has sparked renewed debates about
the ways in which algorithms can perpetuate biases, yielding unintended and
often offensive results.
When Microsoft released the “millennial” chatbot named
Tay in March, it quickly began using racist language and promoting neo-Nazi
views on Twitter. And after Facebook eliminated human editors who had curated
“trending” news stories last month, the algorithm immediately promoted fake and
vulgar stories on news feeds, including one article about a man masturbating
with a chicken sandwich.
While the seemingly racist beauty pageant has prompted
jokes and mockery, computer science experts and social justice advocates say that
in other industries and arenas, the growing use of prejudiced AI systems is no
laughing matter. In some cases, it can have devastating consequences for people
of color.
Beauty.AI – which was created by a “deep learning” group
called Youth Laboratories and supported by Microsoft – relied on large datasets
of photos to build an algorithm that assessed beauty. While there are a number
of reasons why the algorithm favored white people, the main problem was that
the data the project used to establish standards of attractiveness did not
include enough minorities, said Alex Zhavoronkov, Beauty.AI’s chief science
officer.
Although the group did not build the algorithm to treat
light skin as a sign of beauty, the input data effectively led the robot judges
to reach that conclusion.
“If you have not that many people of color within the
dataset, then you might actually have biased results,” said Zhavoronkov, who
said he was surprised by the winners. “When you’re training an algorithm to
recognize certain patterns … you might not have enough data, or the data might
be biased.”
The simplest explanation for biased algorithms is that
the humans who create them have their own deeply entrenched biases. That means
that despite perceptions that algorithms are somehow neutral and uniquely
objective, they can often reproduce and amplify existing prejudices.
The Beauty.AI results offer “the perfect illustration of
the problem”, said Bernard Harcourt, Columbia University professor of law and
political science who has studied “predictive policing”, which has increasingly
relied on machines. “The idea that you could come up with a culturally neutral,
racially neutral conception of beauty is simply mind-boggling.”
The case is a reminder that “humans are really doing the
thinking, even when it’s couched as algorithms and we think it’s neutral and
scientific,” he said.
Civil liberty groups have recently raised concerns that
computer-based law enforcement forecasting tools – which use data to predict
where future crimes will occur – rely on flawed statistics and can exacerbate
racially biased and harmful policing practices.
“It’s polluted data producing polluted results,” said
Malkia Cyril, executive director of the Center for Media Justice.
A ProPublica investigation earlier this year found that
software used to predict future criminals is biased against black people, which
can lead to harsher sentencing.
“That’s truly a matter of somebody’s life is at stake,”
said Sorelle Friedler, a professor of computer science at Haverford College.
A major problem, Friedler said, is that minority groups
by nature are often underrepresented in datasets, which means algorithms can
reach inaccurate conclusions for those populations and the creators won’t detect
it. For example, she said, an algorithm that was biased against Native
Americans could be considered a success given that they are only 2% of the
population.
“You could have a 98% accuracy rate. You would think you
have done a great job on the algorithm.”
Friedler said there are proactive ways algorithms can be
adjusted to correct for biases whether improving input data or implementing
filters to ensure people of different races are receiving equal treatment.
Prejudiced AI programs aren’t limited to the criminal
justice system. One study determined that significantly fewer women than men
were shown online ads for high-paying jobs. Last year, Google’s photo app was
found to have labeled black people as gorillas.
Cyril noted that algorithms are ultimately very limited
in how they can help correct societal inequalities. “We’re overly relying on
technology and algorithms and machine learning when we should be looking at
institutional changes.”
Zhavoronkov said that when Beauty.AI launches another
contest round this fall, he expects the algorithm will have a number of changes
designed to weed out discriminatory results. “We will try to correct it.”
But the reality, he added, is that robots may not be the
best judges of physical appearance: “I was more surprised about how the
algorithm chose the most beautiful people. Out of a very large number, they
chose people who I may not have selected myself.”
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