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Technology has brought us so much:
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the moon landing, the Internet,
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the ability to sequence the human genome.
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But it also taps into a lot of our deepest fears,
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and about 30 years ago,
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the culture critic Neil Postman wrote a book
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called "Amusing Ourselves to Death,"
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which lays this out really brilliantly.
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And here's what he said,
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comparing the dystopian visions
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of George Orwell and Aldous Huxley.
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He said, Orwell feared we would become
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a captive culture.
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Huxley feared we would become a trivial culture.
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Orwell feared the truth would be
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concealed from us,
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and Huxley feared we would be drowned
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in a sea of irrelevance.
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In a nutshell, it's a choice between
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Big Brother watching you
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and you watching Big Brother.
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(Laughter)
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But it doesn't have to be this way.
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We are not passive consumers
of data and technology.
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We shape the role it plays in our lives
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and the way we make meaning from it,
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but to do that,
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we have to pay as much attention to how we think
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as how we code.
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We have to ask questions, and hard questions,
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to move past counting things
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to understanding them.
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We're constantly bombarded with stories
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about how much data there is in the world,
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but when it comes to big data
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and the challenges of interpreting it,
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size isn't everything.
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There's also the speed at which it moves,
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and the many varieties of data types,
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and here are just a few examples:
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images,
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text,
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video,
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audio.
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And what unites this disparate types of data
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is that they're created by people
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and they require context.
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Now, there's a group of data scientists
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out of the University of Illinois-Chicago,
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and they're called the Health Media Collaboratory,
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and they've been working with
the Centers for Disease Control
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to better understand
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how people talk about quitting smoking,
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how they talk about electronic cigarettes,
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and what they can do collectively
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to help them quit.
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The interesting thing is, if you want to understand
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how people talk about smoking,
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first you have to understand
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what they mean when they say "smoking."
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And on Twitter, there are four main categories:
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number one, smoking cigarettes;
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number two, smoking marijuana;
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number three, smoking ribs;
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and number four, smoking hot women.
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(Laughter)
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So then you have to think about, well,
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how do people talk about electronic cigarettes?
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And there are so many different ways
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that people do this, and you can see from the slide
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it's a complex kind of a query.
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And what it reminds us is that
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language is created by people,
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and people are messy and we're complex
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and we use metaphors and slang and jargon
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and we do this 24/7 in many, many languages,
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and then as soon as we figure it out, we change it up.
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So did these ads that the CDC put on,
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these television ads that featured a woman
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with a hole in her throat and that were very graphic
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and very disturbing,
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did they actually have an impact
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on whether people quit?
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And the Health Media Collaboratory
respected the limits of their data,
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but they were able to conclude
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that those advertisements —
and you may have seen them —
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that they had the effect of jolting people
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into a thought process
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that may have an impact on future behavior.
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And what I admire and
appreciate about this project,
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aside from the fact, including the fact
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that it's based on real human need,
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is that it's a fantastic example of courage
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in the face of a sea of irrelevance.
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And so it's not just big data that causes
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challenges of interpretation, because let's face it,
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we human beings have a very rich history
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of taking any amount of data, no matter how small,
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and screwing it up.
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So many years ago, you may remember
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that former President Ronald Reagan
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was very criticized for making a statement
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that facts are stupid things.
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And it was a slip of the tongue, let's be fair.
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He actually meant to quote John Adams' defense
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of British soldiers in the Boston Massacre trials
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that facts are stubborn things.
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But I actually think there's
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a bit of accidental wisdom in what he said,
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because facts are stubborn things,
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but sometimes they're stupid, too.
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I want to tell you a personal story
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about why this matters a lot to me.
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I need to take a breath.
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My son Isaac, when he was two,
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was diagnosed with autism,
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and he was this happy, hilarious,
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loving, affectionate little guy,
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but the metrics on his developmental evaluations,
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which looked at things like
the number of words —
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at that point, none —
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communicative gestures and minimal eye contact,
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put his developmental level
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at that of a nine-month-old baby.
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And the diagnosis was factually correct,
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but it didn't tell the whole story.
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And about a year and a half later,
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when he was almost four,
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I found him in front of the computer one day
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running a Google image search on women,
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spelled "w-i-m-e-n."
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And I did what any obsessed parent would do,
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which is immediately started
hitting the "back" button
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to see what else he'd been searching for.
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And they were, in order: men,
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school, bus and computer.
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And I was stunned,
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because we didn't know that he could spell,
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much less read, and so I asked him,
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"Isaac, how did you do this?"
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And he looked at me very seriously and said,
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"Typed in the box."
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He was teaching himself to communicate,
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but we were looking in the wrong place,
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and this is what happens when assessments
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and analytics overvalue one metric —
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in this case, verbal communication —
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and undervalue others, such
as creative problem-solving.
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Communication was hard for Isaac,
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and so he found a workaround
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to find out what he needed to know.
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And when you think about it, it makes a lot of sense,
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because forming a question
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is a really complex process,
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but he could get himself a lot of the way there
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by putting a word in a search box.
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And so this little moment
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had a really profound impact on me
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and our family
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because it helped us change our frame of reference
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for what was going on with him,
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and worry a little bit less and appreciate
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his resourcefulness more.
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Facts are stupid things.
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And they're vulnerable to misuse,
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willful or otherwise.
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I have a friend, Emily Willingham, who's a scientist,
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and she wrote a piece for Forbes not long ago
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entitled "The 10 Weirdest Things
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Ever Linked to Autism."
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It's quite a list.
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The Internet, blamed for everything, right?
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And of course mothers, because.
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And actually, wait, there's more,
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there's a whole bunch in
the "mother" category here.
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And you can see it's a pretty
rich and interesting list.
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I'm a big fan of
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being pregnant near freeways, personally.
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The final one is interesting,
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because the term "refrigerator mother"
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was actually the original hypothesis
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for the cause of autism,
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and that meant somebody
who was cold and unloving.
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And at this point, you might be thinking,
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"Okay, Susan, we get it,
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you can take data, you can
make it mean anything."
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And this is true, it's absolutely true,
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but the challenge is that
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we have this opportunity
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to try to make meaning out of it ourselves,
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because frankly, data doesn't
create meaning. We do.
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So as businesspeople, as consumers,
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as patients, as citizens,
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we have a responsibility, I think,
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to spend more time
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focusing on our critical thinking skills.
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Why?
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Because at this point in our history, as we've heard
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many times over,
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we can process exabytes of data
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at lightning speed,
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and we have the potential to make bad decisions
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far more quickly, efficiently,
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and with far greater impact than we did in the past.
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Great, right?
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And so what we need to do instead
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is spend a little bit more time
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on things like the humanities
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and sociology, and the social sciences,
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rhetoric, philosophy, ethics,
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because they give us context that is so important
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for big data, and because
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they help us become better critical thinkers.
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Because after all, if I can spot
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a problem in an argument, it doesn't much matter
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whether it's expressed in words or in numbers.
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And this means
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teaching ourselves to find
those confirmation biases
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and false correlations
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and being able to spot a naked emotional appeal
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from 30 yards,
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because something that happens after something
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doesn't mean it happened
because of it, necessarily,
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and if you'll let me geek out on you for a second,
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the Romans called this
"post hoc ergo propter hoc,"
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after which therefore because of which.
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And it means questioning
disciplines like demographics.
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Why? Because they're based on assumptions
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about who we all are based on our gender
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and our age and where we live
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as opposed to data on what
we actually think and do.
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And since we have this data,
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we need to treat it with appropriate privacy controls
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and consumer opt-in,
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and beyond that, we need to be clear
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about our hypotheses,
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the methodologies that we use,
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and our confidence in the result.
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As my high school algebra teacher used to say,
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show your math,
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because if I don't know what steps you took,
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I don't know what steps you didn't take,
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and if I don't know what questions you asked,
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I don't know what questions you didn't ask.
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And it means asking ourselves, really,
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the hardest question of all:
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Did the data really show us this,
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or does the result make us feel
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more successful and more comfortable?
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So the Health Media Collaboratory,
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at the end of their project, they were able
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to find that 87 percent of tweets
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about those very graphic and disturbing
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anti-smoking ads expressed fear,
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but did they conclude
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that they actually made people stop smoking?
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No. It's science, not magic.
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So if we are to unlock
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the power of data,
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we don't have to go blindly into
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Orwell's vision of a totalitarian future,
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or Huxley's vision of a trivial one,
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or some horrible cocktail of both.
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What we have to do
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is treat critical thinking with respect
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and be inspired by examples
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like the Health Media Collaboratory,
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and as they say in the superhero movies,
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let's use our powers for good.
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Thank you.
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(Applause)