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http://www.nytimes.com/2016/05/19/opinion/the-real-bias-built-in-at-facebook.html
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The Real Bias Built In at Facebook
by Zeynep Tufekci
NYT, MAY 19, 2016
FACEBOOK is biased. That's true. But not in the way conservative critics
say it is.
The social network's powerful newsfeed is programmed to be viral,
clicky, upbeat or quarrelsome. That's how its algorithm works, and how
it determines what more than a billion people see every day.
The root of this bias is in algorithms, a much misunderstood but
increasingly powerful method of decision making that is spreading to
fields from news to health care to hiring and even to war.
Algorithms in human affairs are generally complex computer programs that
crunch data and perform computations to optimize outcomes chosen by
programmers. Such an algorithm isn't some pure sifting mechanism,
spitting out objective answers in response to scientific calculations.
Nor is it a mere reflection of the desires of the programmers.
We use these algorithms to explore questions that have no right answer
to begin with, so we don't even have a straightforward way to calibrate
or correct them.
The current discussion of bias and Facebook started this month, after
some former Facebook contractors claimed that the "trending topics"
section on Facebook highlighted stories that were vetted by a small team
of editors who had a prejudice against right-wing news sources.
This suggestion set off a flurry of reactions, and even a letter from
the chairman of the Senate Commerce Committee. However, the trending
topics box is a trivial part of the site, and almost invisible on
mobile, where most people use Facebook. And it is not the newsfeed,
which is controlled by an algorithm.
To defend itself against the charges of bias stemming from the "trending
topics" revelation, Facebook said that the process was neutral, that the
stories were first "surfaced by an algorithm." Mark Zuckerberg, the
chief executive, then invited the radio host Glenn Beck and other
conservatives to meet with him on Wednesday.
But "surfaced by an algorithm" is not a defense of neutrality, because
algorithms aren't neutral.
Algorithms are often presented as an extension of natural sciences like
physics or biology. While these algorithms also use data, math and
computation, they are a fountain of bias and slants -- of a new kind.
If a bridge sways and falls, we can diagnose that as a failure, fault
the engineering, and try to do better next time. If Google shows you
these 11 results instead of those 11, or if a hiring algorithm puts this
person's résumé at the top of a file and not that one, who is to
definitively say what is correct, and what is wrong? Without laws of
nature to anchor them, algorithms used in such subjective decision
making can never be truly neutral, objective or scientific.
Programmers do not, and often cannot, predict what their complex
programs will do. Google's Internet services are billions of lines of
code. Once these algorithms with an enormous number of moving parts are
set loose, they then interact with the world, and learn and react. The
consequences aren't easily predictable.
Our computational methods are also getting more enigmatic. Machine
learning is a rapidly spreading technique that allows computers to
independently learn to learn -- almost as we do as humans -- by churning
through the copious disorganized data, including data we generate in
digital environments.
However, while we now know how to make machines learn, we don't really
know what exact knowledge they have gained. If we did, we wouldn't need
them to learn things themselves: We'd just program the method directly.
With algorithms, we don't have an engineering breakthrough that's making
life more precise, but billions of semi-savant mini-Frankensteins, often
with narrow but deep expertise that we no longer understand, spitting
out answers here and there to questions we can't judge just by numbers,
all under the cloak of objectivity and science.
If these algorithms are not scientifically computing answers to
questions with objective right answers, what are they doing? Mostly,
they "optimize" output to parameters the company chooses, crucially,
under conditions also shaped by the company. On Facebook the goal is to
maximize the amount of engagement you have with the site and keep the
site ad-friendly. You can easily click on "like," for example, but there
is not yet a "this was a challenging but important story" button.
This setup, rather than the hidden personal beliefs of programmers, is
where the thorny biases creep into algorithms, and that's why it's
perfectly plausible for Facebook's work force to be liberal, and yet for
the site to be a powerful conduit for conservative ideas as well as
conspiracy theories and hoaxes -- along with upbeat stories and weighty
debates. Indeed, on Facebook, Donald J. Trump fares better than any
other candidate, and anti-vaccination theories like those peddled by Mr.
Beck easily go viral.
The newsfeed algorithm also values comments and sharing. All this suits
content designed to generate either a sense of oversize delight or
righteous outrage and go viral, hoaxes and conspiracies as well as baby
pictures, happy announcements (that can be liked) and important news and
discussions. Facebook's own research shows that the choices its
algorithm makes can influence people's mood and even affect elections by
shaping turnout.
For example, in August 2014, my analysis found that Facebook's newsfeed
algorithm largely buried news of protests over the killing of Michael
Brown by a police officer in Ferguson, Mo., probably because the story
was certainly not "like"-able and even hard to comment on. Without likes
or comments, the algorithm showed Ferguson posts to fewer people,
generating even fewer likes in a spiral of algorithmic silence. The
story seemed to break through only after many people expressed outrage
on the algorithmically unfiltered Twitter platform, finally forcing the
news to national prominence.
Software giants would like us to believe their algorithms are objective
and neutral, so they can avoid responsibility for their enormous power
as gatekeepers while maintaining as large an audience as possible. Of
course, traditional media organizations face similar pressures to grow
audiences and host ads. At least, though, consumers know that the news
media is not produced in some "neutral" way or above criticism, and a
whole network -- from media watchdogs to public editors -- tries to hold
those institutions accountable.
The first step forward is for Facebook, and anyone who uses algorithms
in subjective decision making, to drop the pretense that they are
neutral. Even Google, whose powerful ranking algorithm can decide the
fate of companies, or politicians, by changing search results, defines
its search algorithms as "computer programs that look for clues to give
you back exactly what you want."
But this is not just about what we want. What we are shown is shaped by
these algorithms, which are shaped by what the companies want from us,
and there is nothing neutral about that.
Zeynep Tufekci is an assistant professor at the School of Information
and Library Science at the University of North Carolina and a
contributing opinion writer.
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