[MUSIC]
Well, one interesting thing about the algorithms that predict future behavior
and psychological traits or intimate traits of people is that, people often
think, my good this is science fiction you know like how could that be or
they say my god this is totally impossible.
Now, it's in fact pretty intuitive.
Look, if you know someone pretty well,
you will be able to predict their future behavior.
Even if you don't know exactly whether they've read a given book or
not, you're reading this book now and you think my God.
The friend of mine, the one that is so well organized and
always talks about this given thing, here she would really love this book.
What you made here, you made a prediction.
Right you know your friend.
You know their psychological traits.
You know some of them intimate traits.
And it allows you to extrapolate from the knowledge you have about the friend into
new areas.
You basically see a new book or see a new play or hear about a new activity and
you can predict which on of your friends would like it.
Now, computers basically computer algorithm do exactly the same thing
they would take set of behaviors that a given person has expressed in the past.
Now match it with behavior of many other people that the algorithm has in
its database and then say, hey you know people who read these books or
people who listen to this type of music or people who visit these websites.
They also tend to vote conservative or study psychology or work as accounts.
So now we can basically, you just make those very simple predictions.
And when I say it simple but then on a large scale,
it basically become very powerful.
A lot of signal that useful for an algorithm would not be useful for
a human being.
Let me give you an example.
If I tell you the politicians that I like,
you'll have no trouble figuring out what my political views are.
Perhaps, even if I gave you ten of my favorite books,
it will be still informative to you as a human being.
Okay, so people who read those books, they tend to have this political leaning,
or this religious leaning, and so on.
But now if I told you, look, those are the ten games I've played recently,
or this is the brand of the shoes that I'm wearing,
now as a human being you'll be like yeah,
it's probably not very revealing by your political views or
religiosity simply because it's kind of difficult for
us to match a brand of a shoe or a game with particular ideology or world view.
Now, the fact that it's difficult for us to see the link,
doesn't mean that the link doesn't exist.
Right? The link could there,
it just could be subtle enough to fall below our threshold of perception.
Now, a computer has a big advantage here.
It can store and process way more data than
humans can in terms of length and their relationships like that.
So it might be that when you look at a huge database of millions of other people,
it might be that people that wear a given brand of shoes they have a slight
liberal or conservative leaning, something that would be imperceptible for humans.
For a computer, it's a useful bit of information.
A useful bit of information that is not conclusive.
But now, if you add up thousands or
millions of little crumbs of inconclusive information like that,
you can in fact arrive at the very accurate profile.
[MUSIC]
To answers this question, we collected a lot of digital footprints and
we use Facebook as our research environment.
We ask people to share of us there Facebook likes, and
Facebook status updates.
And then we also asked the same participants to feel in questionnaires,
personality questionnaires, IQ questionnaires,
happiness questionnaires, and so on.
And then we build machine learning models that would predict the scores of
these people on these questionnaires from the digital footprints.
And once we've done that,
we would take a group of new participants that the algorithm has never seen before.
And we would try to predict their scores
on self imported questionnaires which we used to measure the accuracy
of the algorithmic approach to measure your psychological traits.
And it turns out those methods are very highly accurate.
To give you an example, we compared that curiosity of these algorithms,
of the algorithms based on Facebook likes, with that curiosity of human judges.
We would give questions, personality questions in this case.
To friends and family members of our participants, and would ask them to
fill in those questionnaires in the name of the participants, right?
So based on the acquaintance with the participant who
are supposed to answer the questionnaire in their names.
So, then what you would get?
You would get basic personality scores of the participants,
as predicted by their friends and family members.
And, then on the other hand, we had their scores,
as predicted by the computer algorithm based on their Facebook likes.
And now we could compare, how many likes do you need
to be as accurate as a given friend or family member.
And what we found is that you need only ten likes.
You need to give algorithm only ten likes for
the algorithm to be as accurate as your work put in.
You need around 100 likes for the algorithm to be as accurate when
predicting your personality as your family member like brother or mother would be.
And finally, you need around 200 likes to beat the most accurate of human judges,
your own spouse.
[MUSIC]
So in the past if I wanted to know your political views either I needed to
basically become close with you, and have you share those views with me.
Though, we know that even close friends,
where you often do not discuss certain ideas, and
do not share all of the information, all of their intimate traits with each other.
Now, I could also give you a questionnaire in the past.
And ask you hey can you please fill this questionnaire, and you get
full control over, hey what are you going to respond to this questionnaire or not.
And if you decided to respond to this questionnaire, you still have a choice
of not answering the questions honestly which basically means that you had
full control, one could say, or a large degree of control
over your intimate traits that you did not want to share with the others.
Now, if I can make the same predictions, if I can predict your intimate traits such
as your political views, sexual orientation, religiosity, personality,
intelligence or whether your parents were divorced or not.
If I can predict all of those things by simply providing
an algorithm with the list of songs that you have listened to or
the list of websites that you have visited in recent hours or
days, this basically means that we are heading towards the post-privacy era,
a time where people will not have rights to privacy.
[MUSIC]
When I started studying these algorithms, I was mostly focused on the downside.
I thought it was very scary that.
You can have a computer algorithm that takes seemingly innocent and
seemingly uninformative piece of data such as your playlist on Spotify or
a bunch of Facebook likes, and can extrapolate from these data to make very
accurate predictions about your intimate traits such as your political views,
religiosity, sexual orientation, personality, IQ and so on.
This was pretty scary for me, I did not expect the algorithms to be as accurate.
I thought well, there will be some accuracy, but we quickly learnt that
the accuracy of the algorithms is higher than what people could achieve.
And it also hired then what's could be done with questionnaires,
especially in times of trade that people are not happy to share
with the others like you don't really share your political views or
your sexual orientation with people that you don't know really well.
And I think it only appeared later to me that, there are risks there,
but are also tremendous opportunities, let's say to make the world a safer and
more fair place as in matching people with better jobs or
let's say monitoring their psychological health.
And extending those benefits to people who are traditionally deprived of it.
[MUSIC]
Well, I think that people are really willing to share their data and
give up their data, simply because it's really beneficial to us.
Right, think about Google Maps.
It's an amazing tool that helps us to get from point a to
point b in the shortest possible time and while burning the least fuel.
It's amazing both on the personal level but also on the societal level.
We're saving a lot of hours and a lot of gallons of fuel,
which is also great for environment and so on.
Now, for Google Maps to work, what
basically need to happen is that you can use it to find your way to work but also
you have share the information about how you were driving around with the platform.
Because then the platform can use this data,
that it collected from you, to optimize the routes for other people.
Right, so, I think there's no going back from here.
People get so much in return for sharing the data.
That it's unlikely that they will now willing to give up Google, Google Maps,
Facebook, Amazon, Kindle, Netflix, and all of the other platforms that
basically make our lives easier, safer, more interesting, fulfilling, and so on.
[MUSIC]
Well, I think we certainly need good legislation and
good technology to help us to maintain as much privacy as possible.
But I think that in the long term, the game here is lost.
The sooner we accept that we are heading towards the future without privacy,
the sooner we can focus on trying to organize these societies and
organize the policies technology and
even cultures in such a way as to accommodate the world's Without privacy.
To large extent, it is an uncharted territory.
What in the past required huge institutions,
spying on people, trying to predict their intimate traits,
keeping tabs on them required enormous amount of effort, money and men power.
Today, the same thing can be achieved
by a guy in a dorm room with a laptop and Internet connection.
And a few hundred dollars to spend on databases that can be
purchased on official or an official data markets,
which basically completely changes the game.
And l think the sooner the people accept the fact that basically there is no
privacy going forward, the safer the society would become, the sooner you
realize wherever you are writing your email can be read by motivated others.
The sooner that we all realize that being an atheist or being gay or
being liberal or conservative is something that a basic in people know about,
well, they could probably very easily infer it now and
definitely will just get easier in the future.
The sooner we can focus on making societies more tolerant and open-minded.
Educating the voters to make them more difficult to manipulate and
basically organizing the societies and preparing them for post-privacy era.
[MUSIC]