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 and healthcare to hiring and even war.
Algorithms in human affairs are generally complex computer programs that crunch data and perform computations to optimise 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 and that the stories were first "surfaced by an algorithm". Mark Zuckerberg, the chief executive, then invited radio host Glenn Beck and other conservatives to meet him on Wednesday.
But "surfaced by an algorithm" is not a defence 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, maths 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 resume 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 disorganised 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 "optimise" output to parameters the company chooses, crucially, under conditions also shaped by the company. On Facebook the goal is to maximise 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 set-up, 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 workforce 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 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, Missouri, 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 organisations 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 pretence 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.
NEW YORK TIMES
•The writer is an assistant professor at the School of Information and Library Science at the University of North Carolina.
A version of this article appeared in the print edition of The Straits Times on May 21, 2016, with the headline 'The real bias built in at Facebook'. Print Edition | Subscribe
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