We're building a dystopia just to make people click on ads | Zeynep Tufekci

738,629 views ・ 2017-11-17

TED


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00:12
So when people voice fears of artificial intelligence,
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very often, they invoke images of humanoid robots run amok.
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You know? Terminator?
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You know, that might be something to consider,
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but that's a distant threat.
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Or, we fret about digital surveillance
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with metaphors from the past.
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"1984," George Orwell's "1984,"
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it's hitting the bestseller lists again.
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It's a great book,
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but it's not the correct dystopia for the 21st century.
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What we need to fear most
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is not what artificial intelligence will do to us on its own,
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but how the people in power will use artificial intelligence
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to control us and to manipulate us
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in novel, sometimes hidden,
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subtle and unexpected ways.
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Much of the technology
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that threatens our freedom and our dignity in the near-term future
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is being developed by companies
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in the business of capturing and selling our data and our attention
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to advertisers and others:
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Facebook, Google, Amazon,
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Alibaba, Tencent.
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Now, artificial intelligence has started bolstering their business as well.
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And it may seem like artificial intelligence
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is just the next thing after online ads.
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It's not.
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It's a jump in category.
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It's a whole different world,
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and it has great potential.
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It could accelerate our understanding of many areas of study and research.
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But to paraphrase a famous Hollywood philosopher,
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"With prodigious potential comes prodigious risk."
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Now let's look at a basic fact of our digital lives, online ads.
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Right? We kind of dismiss them.
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They seem crude, ineffective.
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We've all had the experience of being followed on the web
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by an ad based on something we searched or read.
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You know, you look up a pair of boots
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and for a week, those boots are following you around everywhere you go.
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Even after you succumb and buy them, they're still following you around.
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We're kind of inured to that kind of basic, cheap manipulation.
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We roll our eyes and we think, "You know what? These things don't work."
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Except, online,
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the digital technologies are not just ads.
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Now, to understand that, let's think of a physical world example.
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You know how, at the checkout counters at supermarkets, near the cashier,
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there's candy and gum at the eye level of kids?
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That's designed to make them whine at their parents
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just as the parents are about to sort of check out.
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Now, that's a persuasion architecture.
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It's not nice, but it kind of works.
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That's why you see it in every supermarket.
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Now, in the physical world,
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such persuasion architectures are kind of limited,
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because you can only put so many things by the cashier. Right?
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And the candy and gum, it's the same for everyone,
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even though it mostly works
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only for people who have whiny little humans beside them.
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In the physical world, we live with those limitations.
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In the digital world, though,
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persuasion architectures can be built at the scale of billions
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and they can target, infer, understand
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and be deployed at individuals
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one by one
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by figuring out your weaknesses,
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and they can be sent to everyone's phone private screen,
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so it's not visible to us.
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And that's different.
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And that's just one of the basic things that artificial intelligence can do.
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Now, let's take an example.
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Let's say you want to sell plane tickets to Vegas. Right?
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So in the old world, you could think of some demographics to target
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based on experience and what you can guess.
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You might try to advertise to, oh,
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men between the ages of 25 and 35,
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or people who have a high limit on their credit card,
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or retired couples. Right?
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That's what you would do in the past.
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With big data and machine learning,
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that's not how it works anymore.
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So to imagine that,
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think of all the data that Facebook has on you:
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every status update you ever typed,
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every Messenger conversation,
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every place you logged in from,
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all your photographs that you uploaded there.
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If you start typing something and change your mind and delete it,
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Facebook keeps those and analyzes them, too.
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Increasingly, it tries to match you with your offline data.
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It also purchases a lot of data from data brokers.
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It could be everything from your financial records
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to a good chunk of your browsing history.
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Right? In the US, such data is routinely collected,
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collated and sold.
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In Europe, they have tougher rules.
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So what happens then is,
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by churning through all that data, these machine-learning algorithms --
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that's why they're called learning algorithms --
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they learn to understand the characteristics of people
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who purchased tickets to Vegas before.
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When they learn this from existing data,
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they also learn how to apply this to new people.
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So if they're presented with a new person,
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they can classify whether that person is likely to buy a ticket to Vegas or not.
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Fine. You're thinking, an offer to buy tickets to Vegas.
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I can ignore that.
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But the problem isn't that.
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The problem is,
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we no longer really understand how these complex algorithms work.
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We don't understand how they're doing this categorization.
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It's giant matrices, thousands of rows and columns,
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maybe millions of rows and columns,
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and not the programmers
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and not anybody who looks at it,
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even if you have all the data,
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understands anymore how exactly it's operating
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any more than you'd know what I was thinking right now
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if you were shown a cross section of my brain.
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It's like we're not programming anymore,
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we're growing intelligence that we don't truly understand.
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And these things only work if there's an enormous amount of data,
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so they also encourage deep surveillance on all of us
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so that the machine learning algorithms can work.
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That's why Facebook wants to collect all the data it can about you.
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The algorithms work better.
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So let's push that Vegas example a bit.
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What if the system that we do not understand
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was picking up that it's easier to sell Vegas tickets
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to people who are bipolar and about to enter the manic phase.
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Such people tend to become overspenders, compulsive gamblers.
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They could do this, and you'd have no clue that's what they were picking up on.
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I gave this example to a bunch of computer scientists once
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and afterwards, one of them came up to me.
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He was troubled and he said, "That's why I couldn't publish it."
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I was like, "Couldn't publish what?"
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He had tried to see whether you can indeed figure out the onset of mania
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from social media posts before clinical symptoms,
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and it had worked,
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and it had worked very well,
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and he had no idea how it worked or what it was picking up on.
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Now, the problem isn't solved if he doesn't publish it,
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because there are already companies
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that are developing this kind of technology,
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and a lot of the stuff is just off the shelf.
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This is not very difficult anymore.
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Do you ever go on YouTube meaning to watch one video
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and an hour later you've watched 27?
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You know how YouTube has this column on the right
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that says, "Up next"
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and it autoplays something?
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It's an algorithm
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picking what it thinks that you might be interested in
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and maybe not find on your own.
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It's not a human editor.
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It's what algorithms do.
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It picks up on what you have watched and what people like you have watched,
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and infers that that must be what you're interested in,
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what you want more of,
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and just shows you more.
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It sounds like a benign and useful feature,
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except when it isn't.
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So in 2016, I attended rallies of then-candidate Donald Trump
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to study as a scholar the movement supporting him.
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I study social movements, so I was studying it, too.
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And then I wanted to write something about one of his rallies,
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so I watched it a few times on YouTube.
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YouTube started recommending to me
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and autoplaying to me white supremacist videos
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in increasing order of extremism.
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If I watched one,
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it served up one even more extreme
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and autoplayed that one, too.
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If you watch Hillary Clinton or Bernie Sanders content,
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YouTube recommends and autoplays conspiracy left,
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and it goes downhill from there.
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Well, you might be thinking, this is politics, but it's not.
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This isn't about politics.
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This is just the algorithm figuring out human behavior.
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I once watched a video about vegetarianism on YouTube
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and YouTube recommended and autoplayed a video about being vegan.
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It's like you're never hardcore enough for YouTube.
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(Laughter)
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So what's going on?
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Now, YouTube's algorithm is proprietary,
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but here's what I think is going on.
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The algorithm has figured out
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that if you can entice people
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into thinking that you can show them something more hardcore,
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they're more likely to stay on the site
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watching video after video going down that rabbit hole
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while Google serves them ads.
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Now, with nobody minding the ethics of the store,
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these sites can profile people
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who are Jew haters,
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who think that Jews are parasites
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and who have such explicit anti-Semitic content,
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and let you target them with ads.
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They can also mobilize algorithms
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to find for you look-alike audiences,
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people who do not have such explicit anti-Semitic content on their profile
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but who the algorithm detects may be susceptible to such messages,
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and lets you target them with ads, too.
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Now, this may sound like an implausible example,
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but this is real.
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ProPublica investigated this
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and found that you can indeed do this on Facebook,
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and Facebook helpfully offered up suggestions
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on how to broaden that audience.
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BuzzFeed tried it for Google, and very quickly they found,
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yep, you can do it on Google, too.
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And it wasn't even expensive.
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The ProPublica reporter spent about 30 dollars
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to target this category.
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So last year, Donald Trump's social media manager disclosed
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that they were using Facebook dark posts to demobilize people,
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not to persuade them,
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but to convince them not to vote at all.
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And to do that, they targeted specifically,
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for example, African-American men in key cities like Philadelphia,
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and I'm going to read exactly what he said.
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I'm quoting.
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They were using "nonpublic posts
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whose viewership the campaign controls
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so that only the people we want to see it see it.
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We modeled this.
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It will dramatically affect her ability to turn these people out."
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What's in those dark posts?
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We have no idea.
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Facebook won't tell us.
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So Facebook also algorithmically arranges the posts
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that your friends put on Facebook, or the pages you follow.
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It doesn't show you everything chronologically.
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It puts the order in the way that the algorithm thinks will entice you
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to stay on the site longer.
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Now, so this has a lot of consequences.
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You may be thinking somebody is snubbing you on Facebook.
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The algorithm may never be showing your post to them.
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The algorithm is prioritizing some of them and burying the others.
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Experiments show
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that what the algorithm picks to show you can affect your emotions.
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But that's not all.
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It also affects political behavior.
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So in 2010, in the midterm elections,
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Facebook did an experiment on 61 million people in the US
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that was disclosed after the fact.
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So some people were shown, "Today is election day,"
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the simpler one,
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and some people were shown the one with that tiny tweak
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with those little thumbnails
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of your friends who clicked on "I voted."
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This simple tweak.
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OK? So the pictures were the only change,
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and that post shown just once
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turned out an additional 340,000 voters
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in that election,
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according to this research
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as confirmed by the voter rolls.
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A fluke? No.
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Because in 2012, they repeated the same experiment.
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And that time,
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that civic message shown just once
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turned out an additional 270,000 voters.
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For reference, the 2016 US presidential election
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was decided by about 100,000 votes.
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Now, Facebook can also very easily infer what your politics are,
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even if you've never disclosed them on the site.
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Right? These algorithms can do that quite easily.
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What if a platform with that kind of power
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decides to turn out supporters of one candidate over the other?
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How would we even know about it?
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Now, we started from someplace seemingly innocuous --
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online adds following us around --
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and we've landed someplace else.
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As a public and as citizens,
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we no longer know if we're seeing the same information
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or what anybody else is seeing,
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and without a common basis of information,
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little by little,
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public debate is becoming impossible,
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and we're just at the beginning stages of this.
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These algorithms can quite easily infer
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things like your people's ethnicity,
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religious and political views, personality traits,
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intelligence, happiness, use of addictive substances,
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parental separation, age and genders,
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just from Facebook likes.
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These algorithms can identify protesters
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even if their faces are partially concealed.
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These algorithms may be able to detect people's sexual orientation
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just from their dating profile pictures.
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Now, these are probabilistic guesses,
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so they're not going to be 100 percent right,
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but I don't see the powerful resisting the temptation to use these technologies
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just because there are some false positives,
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which will of course create a whole other layer of problems.
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Imagine what a state can do
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with the immense amount of data it has on its citizens.
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China is already using face detection technology
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to identify and arrest people.
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And here's the tragedy:
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we're building this infrastructure of surveillance authoritarianism
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merely to get people to click on ads.
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And this won't be Orwell's authoritarianism.
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This isn't "1984."
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Now, if authoritarianism is using overt fear to terrorize us,
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we'll all be scared, but we'll know it,
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we'll hate it and we'll resist it.
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But if the people in power are using these algorithms
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to quietly watch us,
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to judge us and to nudge us,
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to predict and identify the troublemakers and the rebels,
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to deploy persuasion architectures at scale
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and to manipulate individuals one by one
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using their personal, individual weaknesses and vulnerabilities,
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and if they're doing it at scale
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through our private screens
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so that we don't even know
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what our fellow citizens and neighbors are seeing,
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that authoritarianism will envelop us like a spider's web
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and we may not even know we're in it.
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So Facebook's market capitalization
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is approaching half a trillion dollars.
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It's because it works great as a persuasion architecture.
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But the structure of that architecture
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is the same whether you're selling shoes
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or whether you're selling politics.
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The algorithms do not know the difference.
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The same algorithms set loose upon us
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to make us more pliable for ads
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are also organizing our political, personal and social information flows,
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and that's what's got to change.
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Now, don't get me wrong,
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we use digital platforms because they provide us with great value.
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I use Facebook to keep in touch with friends and family around the world.
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I've written about how crucial social media is for social movements.
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I have studied how these technologies can be used
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to circumvent censorship around the world.
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But it's not that the people who run, you know, Facebook or Google
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are maliciously and deliberately trying
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to make the country or the world more polarized
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and encourage extremism.
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I read the many well-intentioned statements
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that these people put out.
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But it's not the intent or the statements people in technology make that matter,
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it's the structures and business models they're building.
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And that's the core of the problem.
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Either Facebook is a giant con of half a trillion dollars
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and ads don't work on the site,
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it doesn't work as a persuasion architecture,
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or its power of influence is of great concern.
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It's either one or the other.
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It's similar for Google, too.
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So what can we do?
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This needs to change.
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Now, I can't offer a simple recipe,
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because we need to restructure
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the whole way our digital technology operates.
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Everything from the way technology is developed
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to the way the incentives, economic and otherwise,
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20:45
are built into the system.
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We have to face and try to deal with
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the lack of transparency created by the proprietary algorithms,
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the structural challenge of machine learning's opacity,
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all this indiscriminate data that's being collected about us.
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We have a big task in front of us.
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We have to mobilize our technology,
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our creativity
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and yes, our politics
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so that we can build artificial intelligence
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that supports us in our human goals
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21:22
but that is also constrained by our human values.
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And I understand this won't be easy.
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We might not even easily agree on what those terms mean.
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But if we take seriously
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how these systems that we depend on for so much operate,
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I don't see how we can postpone this conversation anymore.
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These structures
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are organizing how we function
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and they're controlling
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21:58
what we can and we cannot do.
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And many of these ad-financed platforms,
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they boast that they're free.
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In this context, it means that we are the product that's being sold.
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We need a digital economy
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where our data and our attention
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is not for sale to the highest-bidding authoritarian or demagogue.
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(Applause)
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So to go back to that Hollywood paraphrase,
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we do want the prodigious potential
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of artificial intelligence and digital technology to blossom,
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but for that, we must face this prodigious menace,
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open-eyed and now.
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Thank you.
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(Applause)
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About this website

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