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<nettime> NYT > Zeynep Tufekci > The Real Bias Built In at Facebook
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<nettime> NYT > Zeynep Tufekci > The Real Bias Built In at Facebook


< http://www.nytimes.com/2016/05/19/opinion/the-real-bias-built-in-at-facebook.html >


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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