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Zeynep Tufekci: We're building a dystopia just to make people click on ads
Zeynep Tufekcy: Stvaramo distopiju samo kako bismo naveli ljude da kliknu na oglase
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Stvaramo distopiju koju pokreće umjetna inteligencija, klip po klik, tvrdi tehnološka sociologinja Zeynep Tufekci. U ovom govoru koji otvara oči, ona objašnjava kako tvrtke poput Facebooka, Googlea i Amazona koriste iste algoritme kako bi vas naveli da kliknete na oglase i kako bi organizirali pristup političkim i društvenim podacima. Strojevi nisu prava prijetnja. Moramo shvatiti kako oni koji imaju moć mogu koristiti umjetnu inteligenciju ne bi li nas kontrolirali - i što mi možemo napraviti kao odgovor na to.
Zeynep Tufekci - Techno-sociologist
Techno-sociologist Zeynep Tufekci asks big questions about our societies and our lives, as both algorithms and digital connectivity spread. Full bio
Techno-sociologist Zeynep Tufekci asks big questions about our societies and our lives, as both algorithms and digital connectivity spread. Full bio
Double-click the English transcript below to play the video.
00:12
So when people voice fears
of artificial intelligence,
of artificial intelligence,
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Kad ljudi izraze svoje strahove
o umjetnoj inteligenciji
o umjetnoj inteligenciji
00:16
very often, they invoke images
of humanoid robots run amok.
of humanoid robots run amok.
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često prizivaju slike
humanoidnih robota koji divljaju.
humanoidnih robota koji divljaju.
00:20
You know? Terminator?
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Znate? Terminator?
00:22
You know, that might be
something to consider,
something to consider,
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Znate, to je nešto
što treba imati na umu,
što treba imati na umu,
00:24
but that's a distant threat.
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ali to je daleka prijetnja.
00:26
Or, we fret about digital surveillance
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Ili se sekiramo zbog digitalnog nadzora
00:30
with metaphors from the past.
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s metaforama iz prošlosti.
00:31
"1984," George Orwell's "1984,"
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"1984", Georgea Orwella, "1984"
00:34
it's hitting the bestseller lists again.
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ponovno je jedna od najčitanijih knjiga.
00:37
It's a great book,
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To je odlična knjiga,
00:39
but it's not the correct dystopia
for the 21st century.
for the 21st century.
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ali to nije točna
distopija 21. stoljeća.
distopija 21. stoljeća.
00:44
What we need to fear most
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Ono čega se moramo najviše bojati
00:45
is not what artificial intelligence
will do to us on its own,
will do to us on its own,
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nije ono što će nam umjetna inteligencija
učiniti sama po sebi,
učiniti sama po sebi,
00:50
but how the people in power
will use artificial intelligence
will use artificial intelligence
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nego to kako će ljudi na vlasti
koristiti umjetnu inteligenciju
koristiti umjetnu inteligenciju
00:55
to control us and to manipulate us
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da nas kontroliraju i manipuliraju nama
00:57
in novel, sometimes hidden,
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na nove, ponekad skrivene,
01:01
subtle and unexpected ways.
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suptilne i neočekivane načine.
01:04
Much of the technology
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Puno tehnologije
01:06
that threatens our freedom
and our dignity in the near-term future
and our dignity in the near-term future
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koja ugrožava našu slobodu
i naše dostojanstvo u bliskoj budućnosti
i naše dostojanstvo u bliskoj budućnosti
01:10
is being developed by companies
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razvijaju društva
01:12
in the business of capturing
and selling our data and our attention
and selling our data and our attention
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koja se bave prikupljanjem i prodajom
naših podataka i naše pozornosti
naših podataka i naše pozornosti
01:17
to advertisers and others:
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oglašivačima i ostalima:
01:19
Facebook, Google, Amazon,
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Facebook, Google, Amazon,
01:22
Alibaba, Tencent.
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Alibaba, Tencent.
01:26
Now, artificial intelligence has started
bolstering their business as well.
bolstering their business as well.
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Umjetna je inteligencija počela
podupirati i njihovo poslovanje.
podupirati i njihovo poslovanje.
01:31
And it may seem
like artificial intelligence
like artificial intelligence
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I može se činiti
da umjetna inteligencija
da umjetna inteligencija
01:33
is just the next thing after online ads.
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slijedi nakon oglasa na internetu.
01:36
It's not.
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Nije.
01:37
It's a jump in category.
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To je posebna kategorija.
01:40
It's a whole different world,
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To je jedan sasvim novi svijet
01:42
and it has great potential.
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i ima velik potencijal.
01:45
It could accelerate our understanding
of many areas of study and research.
of many areas of study and research.
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Može ubrzati naše razumijevanje mnogih
područja proučavanja i istraživanja,
područja proučavanja i istraživanja,
01:53
But to paraphrase
a famous Hollywood philosopher,
a famous Hollywood philosopher,
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ali da parafraziramo
poznatog holivudskog filozofa,
poznatog holivudskog filozofa,
01:56
"With prodigious potential
comes prodigious risk."
comes prodigious risk."
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"S ogromnim potencijalom
dolazi i ogroman rizik."
dolazi i ogroman rizik."
02:01
Now let's look at a basic fact
of our digital lives, online ads.
of our digital lives, online ads.
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Proučimo osnovnu činjenicu naših
digitalnih života, oglase na internetu.
digitalnih života, oglase na internetu.
02:05
Right? We kind of dismiss them.
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Nekako ih ignoriramo.
02:08
They seem crude, ineffective.
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Čine se nedorađeno, neučinkovito.
02:10
We've all had the experience
of being followed on the web
of being followed on the web
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Svi smo se mi suočili
sa situacijom da nas na internetu
sa situacijom da nas na internetu
02:14
by an ad based on something
we searched or read.
we searched or read.
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prati oglas na temelju onoga
što smo tražili ili čitali.
što smo tražili ili čitali.
02:17
You know, you look up a pair of boots
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Znate, tražite čizme
02:18
and for a week, those boots are following
you around everywhere you go.
you around everywhere you go.
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i tjedan dana vas te čizme
slijede gdje god pošli.
slijede gdje god pošli.
02:22
Even after you succumb and buy them,
they're still following you around.
they're still following you around.
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Čak i nakon što podlegnete
i kupite ih, i dalje vas prate.
i kupite ih, i dalje vas prate.
02:26
We're kind of inured to that kind
of basic, cheap manipulation.
of basic, cheap manipulation.
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Na neke smo načine oguglali
na takvu vrstu jeftine manipulacije.
na takvu vrstu jeftine manipulacije.
02:29
We roll our eyes and we think,
"You know what? These things don't work."
"You know what? These things don't work."
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Kolutamo očima i mislimo si,
"Ma znate što? Takve stvari ne pale."
"Ma znate što? Takve stvari ne pale."
02:33
Except, online,
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Osim što, na internetu,
02:35
the digital technologies are not just ads.
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digitalne tehnologije nisu samo oglasi.
02:40
Now, to understand that,
let's think of a physical world example.
let's think of a physical world example.
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Kako biste to razumjeli,
proučimo primjer fizičkog svijeta.
proučimo primjer fizičkog svijeta.
02:43
You know how, at the checkout counters
at supermarkets, near the cashier,
at supermarkets, near the cashier,
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Znate kako u redovima
u supermarketima, pored blagajni
u supermarketima, pored blagajni
02:48
there's candy and gum
at the eye level of kids?
at the eye level of kids?
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stoje slatkiši i žvakaće
u visini očiju djece?
u visini očiju djece?
02:52
That's designed to make them
whine at their parents
whine at their parents
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Osmišljeni su kako bi natjerali djecu
02:56
just as the parents
are about to sort of check out.
are about to sort of check out.
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da gnjave već izmorene roditelje.
03:00
Now, that's a persuasion architecture.
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To je arhitektura uvjeravanja.
03:03
It's not nice, but it kind of works.
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Nije lijepa, ali funkcionira.
03:06
That's why you see it
in every supermarket.
in every supermarket.
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Zato je možete vidjeti posvuda.
03:08
Now, in the physical world,
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U fizičkom svijetu
03:10
such persuasion architectures
are kind of limited,
are kind of limited,
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te su arhitekture uvjeravanja ograničene
03:12
because you can only put
so many things by the cashier. Right?
so many things by the cashier. Right?
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jer ne možete puno toga
staviti pored blagajne. Zar ne?
staviti pored blagajne. Zar ne?
03:17
And the candy and gum,
it's the same for everyone,
it's the same for everyone,
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Slatkiši i žvakaće isti su za sve
03:22
even though it mostly works
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iako uglavnom funkcioniraju
03:23
only for people who have
whiny little humans beside them.
whiny little humans beside them.
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samo na ljudima koji
pored sebe imaju male gnjavatore.
pored sebe imaju male gnjavatore.
03:29
In the physical world,
we live with those limitations.
we live with those limitations.
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U fizičkom svijetu
živimo s takvim ograničenjima.
živimo s takvim ograničenjima.
03:34
In the digital world, though,
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U digitalnom svijetu, doduše,
03:36
persuasion architectures
can be built at the scale of billions
can be built at the scale of billions
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arhitekture uvjeravanja mogu se
izgraditi na ljestvici od milijardi
izgraditi na ljestvici od milijardi
03:41
and they can target, infer, understand
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i mogu naciljati, zaključiti, razumjeti
03:45
and be deployed at individuals
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te ih mogu koristiti pojedinci
03:48
one by one
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jedan po jedan
03:49
by figuring out your weaknesses,
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otkrivajući naše slabosti
03:52
and they can be sent
to everyone's phone private screen,
to everyone's phone private screen,
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i mogu se slati svima
na privatne mobitele,
na privatne mobitele,
03:57
so it's not visible to us.
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pa nam nije vidljivo.
03:59
And that's different.
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A to je drugačije.
04:01
And that's just one of the basic things
that artificial intelligence can do.
that artificial intelligence can do.
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To je samo jedna od osnovnih stvari
koje umjetna inteligencija može raditi.
koje umjetna inteligencija može raditi.
04:04
Now, let's take an example.
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Uzmimo primjer.
04:06
Let's say you want to sell
plane tickets to Vegas. Right?
plane tickets to Vegas. Right?
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Recimo da želite prodati
avionske karte za Vegas. Dobro?
avionske karte za Vegas. Dobro?
04:08
So in the old world, you could think
of some demographics to target
of some demographics to target
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U starom svijetu mogli ste
razmišljati o ciljanoj populaciji
razmišljati o ciljanoj populaciji
04:12
based on experience
and what you can guess.
and what you can guess.
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ovisno o iskustvu
i o tome što možete pretpostaviti.
i o tome što možete pretpostaviti.
04:15
You might try to advertise to, oh,
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Mogli biste postaviti oglas za
04:18
men between the ages of 25 and 35,
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muškarce između 25. i 35. godine
04:20
or people who have
a high limit on their credit card,
a high limit on their credit card,
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ili za ljude koji imaju
visok dozvoljeni minus na kartici
visok dozvoljeni minus na kartici
04:24
or retired couples. Right?
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ili za umirovljene parove.
04:26
That's what you would do in the past.
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To biste radili u prošlosti.
04:28
With big data and machine learning,
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Zahvaljujući ogromnim
količinama podataka i strojnom učenju,
količinama podataka i strojnom učenju,
04:31
that's not how it works anymore.
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to više ne ide tako.
04:33
So to imagine that,
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Kako biste to zamislili,
04:35
think of all the data
that Facebook has on you:
that Facebook has on you:
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razmišljajte o svim podacima
koje Facebook ima o vama:
koje Facebook ima o vama:
04:39
every status update you ever typed,
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svaki status koji ste ikad napisali,
04:41
every Messenger conversation,
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svaki razgovor na chatu,
04:44
every place you logged in from,
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svako mjesto na kojem ste se logirali,
04:48
all your photographs
that you uploaded there.
that you uploaded there.
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sve vaše fotografije
koje ste ondje objavili.
koje ste ondje objavili.
04:51
If you start typing something
and change your mind and delete it,
and change your mind and delete it,
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Ako počnete nešto tipkati,
predomislite se i obrišete to,
predomislite se i obrišete to,
04:55
Facebook keeps those
and analyzes them, too.
and analyzes them, too.
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Facebook će i to
čuvati i analizirati.
čuvati i analizirati.
04:59
Increasingly, it tries
to match you with your offline data.
to match you with your offline data.
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Sve vas više pokušava povezati
s vašim izvanmrežnim podacima.
s vašim izvanmrežnim podacima.
05:03
It also purchases
a lot of data from data brokers.
a lot of data from data brokers.
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Također kupuje puno podataka
od posrednika podacima.
od posrednika podacima.
05:06
It could be everything
from your financial records
from your financial records
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Može kupiti bilo što
od vaših financijskih izvještaja
od vaših financijskih izvještaja
05:09
to a good chunk of your browsing history.
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do većeg dijela vaše
povijesti pretraživanja.
povijesti pretraživanja.
05:12
Right? In the US,
such data is routinely collected,
such data is routinely collected,
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U SAD-u se takvi podaci
rutinski prikupljaju,
rutinski prikupljaju,
05:17
collated and sold.
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razvrstavaju i prodaju.
05:20
In Europe, they have tougher rules.
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U Europi su pravila stroža.
05:23
So what happens then is,
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Događa se to
05:26
by churning through all that data,
these machine-learning algorithms --
these machine-learning algorithms --
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da prekopavanjem svih tih podataka
ti algoritmi za strojno učenje --
ti algoritmi za strojno učenje --
05:30
that's why they're called
learning algorithms --
learning algorithms --
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zato se zovu algoritmi učenja -
05:33
they learn to understand
the characteristics of people
the characteristics of people
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uče razumijevati karakteristike ljudi
05:38
who purchased tickets to Vegas before.
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koji su prije kupili karte za Vegas.
05:41
When they learn this from existing data,
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Kad to nauče iz postojećih podataka,
05:45
they also learn
how to apply this to new people.
how to apply this to new people.
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također nauče kako to
primjenjivati na nove ljude.
primjenjivati na nove ljude.
05:49
So if they're presented with a new person,
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Pa ako se nađu pred novom osobom,
05:52
they can classify whether that person
is likely to buy a ticket to Vegas or not.
is likely to buy a ticket to Vegas or not.
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mogu odrediti hoće li ta osoba
kupiti kartu za Vegas ili ne.
kupiti kartu za Vegas ili ne.
05:57
Fine. You're thinking,
an offer to buy tickets to Vegas.
an offer to buy tickets to Vegas.
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Dobro. Mislite,
ponuda da kupim karte za Vegas.
ponuda da kupim karte za Vegas.
06:03
I can ignore that.
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Mogu to ignorirati.
06:04
But the problem isn't that.
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Ali nije problem u tome.
06:06
The problem is,
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Problem je u tome
06:08
we no longer really understand
how these complex algorithms work.
how these complex algorithms work.
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što više ne shvaćamo
kako ti složeni algoritmi funkcioniraju.
kako ti složeni algoritmi funkcioniraju.
06:12
We don't understand
how they're doing this categorization.
how they're doing this categorization.
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Ne razumijemo
kako rade tu kategorizaciju.
kako rade tu kategorizaciju.
06:16
It's giant matrices,
thousands of rows and columns,
thousands of rows and columns,
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Njegove ogromne matrice,
tisuće redova i stupaca,
tisuće redova i stupaca,
06:20
maybe millions of rows and columns,
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možda milijune redova i stupaca,
06:23
and not the programmers
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a ni programeri,
06:26
and not anybody who looks at it,
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a ni bilo tko tko to pogleda,
06:29
even if you have all the data,
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čak i ako imate sve podatke,
06:30
understands anymore
how exactly it's operating
how exactly it's operating
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više ne razumije
kako to točno funkcionira
kako to točno funkcionira
06:35
any more than you'd know
what I was thinking right now
what I was thinking right now
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bolje nego što bi vi znali
o čemu ja upravo razmišljam
o čemu ja upravo razmišljam
06:39
if you were shown
a cross section of my brain.
a cross section of my brain.
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da vam pokažu
presjek mog mozga.
presjek mog mozga.
06:44
It's like we're not programming anymore,
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Kao da više ne programiramo,
06:46
we're growing intelligence
that we don't truly understand.
that we don't truly understand.
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već uzgajamo inteligenciju
koju baš i ne razumijemo.
koju baš i ne razumijemo.
06:52
And these things only work
if there's an enormous amount of data,
if there's an enormous amount of data,
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A takve stvari funkcioniraju samo
kada imamo ogromne količine podataka,
kada imamo ogromne količine podataka,
06:56
so they also encourage
deep surveillance on all of us
deep surveillance on all of us
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pa također potiču
na značajan nadzor svih nas
na značajan nadzor svih nas
07:01
so that the machine learning
algorithms can work.
algorithms can work.
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da algoritmi za strojno učenje
mogu funkcionirati.
mogu funkcionirati.
07:04
That's why Facebook wants
to collect all the data it can about you.
to collect all the data it can about you.
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Zato Facebook želi
prikupiti što više podataka o vama.
prikupiti što više podataka o vama.
07:07
The algorithms work better.
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Algoritmi bolje funkcioniraju.
07:08
So let's push that Vegas example a bit.
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Pozabavimo se još malo primjerom Vegasa.
07:11
What if the system
that we do not understand
that we do not understand
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Što ako je sustav koji ne razumijemo
07:16
was picking up that it's easier
to sell Vegas tickets
to sell Vegas tickets
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shvatio da bi bilo lakše
prodati karte za Vegas
prodati karte za Vegas
07:21
to people who are bipolar
and about to enter the manic phase.
and about to enter the manic phase.
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bipolarnim osobama
koje upravo ulaze u fazu manije?
koje upravo ulaze u fazu manije?
07:25
Such people tend to become
overspenders, compulsive gamblers.
overspenders, compulsive gamblers.
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Takvi ljudi prekomjerno
troše i kompulzivno kockaju.
troše i kompulzivno kockaju.
07:31
They could do this, and you'd have no clue
that's what they were picking up on.
that's what they were picking up on.
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Mogli bi to napraviti, a vi ne biste
imali pojma da tako djeluju.
imali pojma da tako djeluju.
07:35
I gave this example
to a bunch of computer scientists once
to a bunch of computer scientists once
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Ovaj sam primjer jednom dala
većem broju kompjutorskih znanstvenika
većem broju kompjutorskih znanstvenika
07:39
and afterwards, one of them came up to me.
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i nakon toga mi je jedan od njih prišao.
07:41
He was troubled and he said,
"That's why I couldn't publish it."
"That's why I couldn't publish it."
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Bio je uznemiren i rekao je,
"Zato to nisam mogao objaviti."
"Zato to nisam mogao objaviti."
07:45
I was like, "Couldn't publish what?"
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Rekoh "Što to?"
07:47
He had tried to see whether you can indeed
figure out the onset of mania
figure out the onset of mania
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Pokušao je vidjeti
može li se prepoznati početak faze manije
može li se prepoznati početak faze manije
07:53
from social media posts
before clinical symptoms,
before clinical symptoms,
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iz objava na društvenim mrežama
prije kliničkih znakova,
prije kliničkih znakova,
07:56
and it had worked,
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i to je funkcioniralo,
07:58
and it had worked very well,
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i to jako dobro,
08:00
and he had no idea how it worked
or what it was picking up on.
or what it was picking up on.
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a nije imao pojma kako je
funkcioniralo ili na što je reagiralo.
funkcioniralo ili na što je reagiralo.
08:06
Now, the problem isn't solved
if he doesn't publish it,
if he doesn't publish it,
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Ali problem nije riješen
ako to ne objavi
ako to ne objavi
08:11
because there are already companies
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jer već postoje tvrtke
08:13
that are developing
this kind of technology,
this kind of technology,
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koje razvijaju
ovakvu vrstu tehnologije,
ovakvu vrstu tehnologije,
08:15
and a lot of the stuff
is just off the shelf.
is just off the shelf.
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a puno je stvari raspoloživo.
08:19
This is not very difficult anymore.
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To više nije teško.
08:21
Do you ever go on YouTube
meaning to watch one video
meaning to watch one video
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Jeste li ikada otišli na YouTube
s namjerom da pogledate jedan video
s namjerom da pogledate jedan video
08:25
and an hour later you've watched 27?
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i sat vremena kasnije pogledali ste ih 27?
08:28
You know how YouTube
has this column on the right
has this column on the right
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Znate kako YouTube
s desne strane ime stupac
s desne strane ime stupac
08:31
that says, "Up next"
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"Sljedeće"
08:33
and it autoplays something?
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i automatski reproducira nešto?
08:35
It's an algorithm
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To je algoritam
08:36
picking what it thinks
that you might be interested in
that you might be interested in
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koji bira ono što misli
da biste vi htjeli pogledati,
da biste vi htjeli pogledati,
08:40
and maybe not find on your own.
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a što ne biste
uspjeli naći sami.
uspjeli naći sami.
08:41
It's not a human editor.
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To nije ljudski uređivač.
08:43
It's what algorithms do.
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Algoritmi tako funkcioniraju.
08:44
It picks up on what you have watched
and what people like you have watched,
and what people like you have watched,
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Prati što ste gledali
i što su gledali ljudi poput vas
i što su gledali ljudi poput vas
08:49
and infers that that must be
what you're interested in,
what you're interested in,
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i zaključuje da i vas
to vjerojatno zanima,
to vjerojatno zanima,
08:53
what you want more of,
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da želite još toga,
08:54
and just shows you more.
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pa vam pokazuje više toga.
08:56
It sounds like a benign
and useful feature,
and useful feature,
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Zvuči kao nevina
i korisna značajka,
i korisna značajka,
08:59
except when it isn't.
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ali nije.
09:01
So in 2016, I attended rallies
of then-candidate Donald Trump
of then-candidate Donald Trump
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Godine 2016. bila sam na skupu
tadašnjeg kandidata Donalda Trumpa
tadašnjeg kandidata Donalda Trumpa
09:09
to study as a scholar
the movement supporting him.
the movement supporting him.
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kako bih kao naučnik
proučavala pokret koji ga podržava.
proučavala pokret koji ga podržava.
09:13
I study social movements,
so I was studying it, too.
so I was studying it, too.
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Proučavam društvene pokrete,
pa sam i to proučavala.
pa sam i to proučavala.
09:16
And then I wanted to write something
about one of his rallies,
about one of his rallies,
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Tada sam htjela napisati nešto
o jednom od njegovih skupova,
o jednom od njegovih skupova,
09:20
so I watched it a few times on YouTube.
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pa sam ga pogledala
nekoliko puta na YT-u.
nekoliko puta na YT-u.
09:23
YouTube started recommending to me
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YouTube mi je počeo preporučivati
09:26
and autoplaying to me
white supremacist videos
white supremacist videos
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i automatski puštati
videe o nadmoći bijele rase
videe o nadmoći bijele rase
09:30
in increasing order of extremism.
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koji su išli prema ekstremizmu.
09:33
If I watched one,
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Ako bih pogledala jedan,
09:35
it served up one even more extreme
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ponudio bi mi jedan još ekstremniji
09:38
and autoplayed that one, too.
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i automatski ga puštao.
09:40
If you watch Hillary Clinton
or Bernie Sanders content,
or Bernie Sanders content,
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Ako gledate sadržaje
Hillary Clinton ili Bernija Sandersa,
Hillary Clinton ili Bernija Sandersa,
09:44
YouTube recommends
and autoplays conspiracy left,
and autoplays conspiracy left,
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Youtube vam preporučuje
i pušta zavjeru ljevice
i pušta zavjeru ljevice
09:49
and it goes downhill from there.
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i od toga ide samo nagore.
09:52
Well, you might be thinking,
this is politics, but it's not.
this is politics, but it's not.
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Možda mislite
da je to politika, ali nije.
da je to politika, ali nije.
09:55
This isn't about politics.
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Tu se ne radi o politici.
09:56
This is just the algorithm
figuring out human behavior.
figuring out human behavior.
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Ovo je samo algoritam
koji tumači ljudsko ponašanje.
koji tumači ljudsko ponašanje.
09:59
I once watched a video
about vegetarianism on YouTube
about vegetarianism on YouTube
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Jednom sam pogledala
video o vegetarijanstvu
video o vegetarijanstvu
10:04
and YouTube recommended
and autoplayed a video about being vegan.
and autoplayed a video about being vegan.
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i Youtube mi je preporučio
i pustio video o veganstvu.
i pustio video o veganstvu.
10:09
It's like you're never
hardcore enough for YouTube.
hardcore enough for YouTube.
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Kad da nikad niste
dovoljno žestoki zagovornici ičega.
dovoljno žestoki zagovornici ičega.
10:12
(Laughter)
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(Smijeh)
10:14
So what's going on?
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Što se događa?
10:16
Now, YouTube's algorithm is proprietary,
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YouTubeov algoritam je privatan,
10:20
but here's what I think is going on.
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ali mislim da se ovo događa.
10:23
The algorithm has figured out
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Algoritam je shvatio
10:25
that if you can entice people
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da ako možete navesti ljude
10:29
into thinking that you can
show them something more hardcore,
show them something more hardcore,
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da misle da im možete
pokazati nešto još žešće,
pokazati nešto još žešće,
10:32
they're more likely to stay on the site
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skloniji su tome da ostanu na stranici
10:35
watching video after video
going down that rabbit hole
going down that rabbit hole
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gledajući video za videom
ulazeći sve dublje u rupu bez dna
ulazeći sve dublje u rupu bez dna
10:39
while Google serves them ads.
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dok im Google pokazuje oglase.
10:43
Now, with nobody minding
the ethics of the store,
the ethics of the store,
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Dok nikome na pazi
na etničku strukturu u trgovini,
na etničku strukturu u trgovini,
10:47
these sites can profile people
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ove stranice mogu isprofilirati ljude
10:53
who are Jew haters,
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koji su mrzitelji židova,
10:56
who think that Jews are parasites
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koji misle da su židovi paraziti
11:00
and who have such explicit
anti-Semitic content,
anti-Semitic content,
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i koji imaju takav
eksplicitan antisemitski sadržaj
eksplicitan antisemitski sadržaj
11:06
and let you target them with ads.
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i dati vam da oglase
usmjerite prema njima.
usmjerite prema njima.
11:09
They can also mobilize algorithms
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Također mogu mobilizirati algoritme
11:12
to find for you look-alike audiences,
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kako bi pronašli publiku sličnu vama,
11:15
people who do not have such explicit
anti-Semitic content on their profile
anti-Semitic content on their profile
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ljude koji nemaju takav eksplicitni
antisemitski sadržaj na svom profilu,
antisemitski sadržaj na svom profilu,
11:21
but who the algorithm detects
may be susceptible to such messages,
may be susceptible to such messages,
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ali koje algoritam prepoznaje kao osobe
koje su osjetljive na takve porukama
koje su osjetljive na takve porukama
11:27
and lets you target them with ads, too.
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i dopušta da na njih
usmjeravate oglase.
usmjeravate oglase.
11:30
Now, this may sound
like an implausible example,
like an implausible example,
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Ovo će možda zvučati
kao nevjerojatan primjer,
kao nevjerojatan primjer,
11:33
but this is real.
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ali stvaran je.
11:35
ProPublica investigated this
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ProPublica je to istraživala
11:37
and found that you can indeed
do this on Facebook,
do this on Facebook,
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i otkrila je da to stvarno
možete raditi na Facebooku
možete raditi na Facebooku
11:41
and Facebook helpfully
offered up suggestions
offered up suggestions
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i Facebook je dobronamjerno
ponudio prijedloge
ponudio prijedloge
11:43
on how to broaden that audience.
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kako proširiti takvu publiku.
11:46
BuzzFeed tried it for Google,
and very quickly they found,
and very quickly they found,
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BuzzFeed je to isprobao
na Googleu i brzo su otkrili
na Googleu i brzo su otkrili
11:49
yep, you can do it on Google, too.
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da to možete raditi i na Googleu.
11:51
And it wasn't even expensive.
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Čak nije bilo ni skupo.
11:53
The ProPublica reporter
spent about 30 dollars
spent about 30 dollars
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Reporter ProPublice
potrošio je 30-ak dolara
potrošio je 30-ak dolara
11:57
to target this category.
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kako bi se oglašavao ovoj kategoriji.
12:02
So last year, Donald Trump's
social media manager disclosed
social media manager disclosed
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Prošle godine upravitelj društvenih medija
Donalda Trumpa priopćio je
Donalda Trumpa priopćio je
12:07
that they were using Facebook dark posts
to demobilize people,
to demobilize people,
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da su koristili tamne postove
na Facebooku kako bi demobilizirali ljude,
na Facebooku kako bi demobilizirali ljude,
12:13
not to persuade them,
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ne kako bi ih nagovorili,
12:14
but to convince them not to vote at all.
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već kako bi ih uvjerili
da uopće ne glasaju.
da uopće ne glasaju.
12:18
And to do that,
they targeted specifically,
they targeted specifically,
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Kako bi to učinili,
posebno su se usmjerili na
posebno su se usmjerili na
12:22
for example, African-American men
in key cities like Philadelphia,
in key cities like Philadelphia,
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npr. afro-amerikance
u ključnim gradovima poput Philadelphije
u ključnim gradovima poput Philadelphije
12:26
and I'm going to read
exactly what he said.
exactly what he said.
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i pročitat ću
što je točno rekao.
što je točno rekao.
12:28
I'm quoting.
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Citiram.
12:29
They were using "nonpublic posts
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Koristili su "privatne objave
12:32
whose viewership the campaign controls
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čiju publiku određuje kampanja
12:35
so that only the people
we want to see it see it.
we want to see it see it.
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kako bi ih vidjeli samo ljudi
za koje želimo da ih vide.
za koje želimo da ih vide.
12:38
We modeled this.
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Modelirali smo to.
12:40
It will dramatically affect her ability
to turn these people out."
to turn these people out."
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Dramatično će utjecati
na sposobnost da te ljude isključi."
na sposobnost da te ljude isključi."
12:45
What's in those dark posts?
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Što je u tim tamnim postovima?
12:48
We have no idea.
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Nemamo pojma.
12:50
Facebook won't tell us.
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Facebook nam neće reći.
12:52
So Facebook also algorithmically
arranges the posts
arranges the posts
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Facebook također
putem algoritma razvrstava objave
putem algoritma razvrstava objave
12:56
that your friends put on Facebook,
or the pages you follow.
or the pages you follow.
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koje vaši prijatelji stavljaju
na Facebook, ili stranice koje pratite.
na Facebook, ili stranice koje pratite.
13:00
It doesn't show you
everything chronologically.
everything chronologically.
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Ne pokazuje vam
sve kronološkim slijedom.
sve kronološkim slijedom.
13:02
It puts the order in the way
that the algorithm thinks will entice you
that the algorithm thinks will entice you
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Redoslijed je takav
da vas algoritam želi namamiti
da vas algoritam želi namamiti
13:07
to stay on the site longer.
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da duže ostanete na stranici.
13:11
Now, so this has a lot of consequences.
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To ima jako puno posljedica.
13:14
You may be thinking
somebody is snubbing you on Facebook.
somebody is snubbing you on Facebook.
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Možete misliti da vas netko
ignorira na Facebooku.
ignorira na Facebooku.
13:18
The algorithm may never
be showing your post to them.
be showing your post to them.
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Algoritam možda nikada
ne pokaže vašu objavu njima.
ne pokaže vašu objavu njima.
13:22
The algorithm is prioritizing
some of them and burying the others.
some of them and burying the others.
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Algoritam stavlja prioritet
na neke od njih, a druge zakopava.
na neke od njih, a druge zakopava.
13:29
Experiments show
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Eksperimenti pokazuju
13:30
that what the algorithm picks to show you
can affect your emotions.
can affect your emotions.
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da ono što vam algoritam odluči pokazati
može utjecati na vaše emocije.
može utjecati na vaše emocije.
13:36
But that's not all.
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Ali to nije sve.
13:38
It also affects political behavior.
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Također utječe na vaše
političko ponašanje.
političko ponašanje.
13:41
So in 2010, in the midterm elections,
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Godine 2010. u prvom dijelu izbora,
13:46
Facebook did an experiment
on 61 million people in the US
on 61 million people in the US
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Facebook je proveo eksperiment
na 61 milijunu ljudi u SAD-u
na 61 milijunu ljudi u SAD-u
13:51
that was disclosed after the fact.
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o kojem su izvijestili kad je završio.
13:53
So some people were shown,
"Today is election day,"
"Today is election day,"
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Nekim su ljudima pokazivali
"Danas su izbori",
"Danas su izbori",
13:57
the simpler one,
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jednostavniju poruku,
13:58
and some people were shown
the one with that tiny tweak
the one with that tiny tweak
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a nekima su pokazivali
neznatno izmijenjenu poruku
neznatno izmijenjenu poruku
14:02
with those little thumbnails
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sa sličicama
14:04
of your friends who clicked on "I voted."
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vaših prijatelja koji su
kliknuli na "Glasao sam".
kliknuli na "Glasao sam".
14:09
This simple tweak.
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Ta jednostavna izmjena.
14:11
OK? So the pictures were the only change,
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Dobro? Slike su bile jedina izmjena,
14:15
and that post shown just once
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a to što je ta objava pokazana samo jednom
14:19
turned out an additional 340,000 voters
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potaknula je dodatnih 340.000 birača
14:25
in that election,
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da glasuju na tim izborima,
14:26
according to this research
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prema ovom istraživanju,
14:28
as confirmed by the voter rolls.
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što je potvrđeno popisom birača.
14:32
A fluke? No.
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Slučajnost? Ne.
14:34
Because in 2012,
they repeated the same experiment.
they repeated the same experiment.
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Godine 2012. ponovili su taj eksperiment.
14:40
And that time,
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I tada
14:42
that civic message shown just once
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građanska poruka
koja je prikazana samo jednom
koja je prikazana samo jednom
14:45
turned out an additional 270,000 voters.
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potaknula je dodatnih 270.000 birača.
14:51
For reference, the 2016
US presidential election
US presidential election
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Za usporedbu,
predsjedničke izbore 2016. u SAD-u
predsjedničke izbore 2016. u SAD-u
14:56
was decided by about 100,000 votes.
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razriješilo je 100.000 glasova.
15:01
Now, Facebook can also
very easily infer what your politics are,
very easily infer what your politics are,
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Facebook također može vrlo lako
zaključiti vaše političko opredjeljenje,
zaključiti vaše političko opredjeljenje,
15:06
even if you've never
disclosed them on the site.
disclosed them on the site.
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čak i ako ga nikad niste
objavili na stranici.
objavili na stranici.
15:08
Right? These algorithms
can do that quite easily.
can do that quite easily.
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Ti algoritmi to mogu odraditi vrlo lako.
15:11
What if a platform with that kind of power
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Što ako platforma s takvom moći
15:15
decides to turn out supporters
of one candidate over the other?
of one candidate over the other?
270
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odluči okrenuti pristalice
jednog kandidata na štetu drugoga?
jednog kandidata na štetu drugoga?
15:21
How would we even know about it?
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Kako bismo uopće znali da se to dogodilo?
15:25
Now, we started from someplace
seemingly innocuous --
seemingly innocuous --
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Počeli smo od nečega
naizgled bezopasnog --
naizgled bezopasnog --
15:29
online adds following us around --
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oglasi na internetu
što nas slijede okolo --
što nas slijede okolo --
15:31
and we've landed someplace else.
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i došli na jedno sasvim drugo mjesto.
15:35
As a public and as citizens,
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Kao javnost i kao građani,
15:37
we no longer know
if we're seeing the same information
if we're seeing the same information
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više ne znamo gledamo li iste informacije
15:41
or what anybody else is seeing,
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ili ono što svi ostali gledaju
15:43
and without a common basis of information,
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i bez zajedničke baze podataka,
15:46
little by little,
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malo po malo,
15:47
public debate is becoming impossible,
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javne debate postaju nemoguće
15:51
and we're just at
the beginning stages of this.
the beginning stages of this.
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i opet smo na početku.
15:54
These algorithms can quite easily infer
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Ovi algoritmi mogu vrlo lako zaključiti
15:57
things like your people's ethnicity,
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o stvarima poput etničkog podrijetla,
16:00
religious and political views,
personality traits,
personality traits,
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religijskim, političkim nazorima,
crtama ličnosti,
crtama ličnosti,
16:03
intelligence, happiness,
use of addictive substances,
use of addictive substances,
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inteligenciji, sreći,
korištenju tvari koje izazivaju ovisnost,
korištenju tvari koje izazivaju ovisnost,
16:06
parental separation, age and genders,
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rastavljenosti roditelja, dobi i spolu,
16:09
just from Facebook likes.
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i to sve na osnovu lajkova na Facebooku.
16:13
These algorithms can identify protesters
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Ti algoritmi mogu prepoznati prosvjednike,
16:17
even if their faces
are partially concealed.
are partially concealed.
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čak i ako su njihova lica
djelomično skrivena.
djelomično skrivena.
16:21
These algorithms may be able
to detect people's sexual orientation
to detect people's sexual orientation
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Ti algoritmi mogu prepoznati
seksualnu orijentaciju ljudi
seksualnu orijentaciju ljudi
16:28
just from their dating profile pictures.
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prema njihovim slikama
na stranicama za pronalaženje partnera.
na stranicama za pronalaženje partnera.
16:33
Now, these are probabilistic guesses,
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To su probabilističke pretpostavke,
16:36
so they're not going
to be 100 percent right,
to be 100 percent right,
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pa neće biti 100% istinite,
16:39
but I don't see the powerful resisting
the temptation to use these technologies
the temptation to use these technologies
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ali ne vidim snažno opiranje
iskušenjima da se koriste te tehnologije
iskušenjima da se koriste te tehnologije
16:44
just because there are
some false positives,
some false positives,
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jer postoje neki lažno pozitivni nalazi
16:46
which will of course create
a whole other layer of problems.
a whole other layer of problems.
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koji će, naravno,
stvoriti cijeli niz problema.
stvoriti cijeli niz problema.
16:49
Imagine what a state can do
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Zamislite što država može napraviti
16:52
with the immense amount of data
it has on its citizens.
it has on its citizens.
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s ogromnim količinama podataka
koje ima o svojim građanima.
koje ima o svojim građanima.
16:56
China is already using
face detection technology
face detection technology
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Kina već koristi
tehnologiju prepoznavanja lica
tehnologiju prepoznavanja lica
17:01
to identify and arrest people.
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kako bi identificirala i uhitila ljude.
17:05
And here's the tragedy:
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A u ovome je tragedija:
17:07
we're building this infrastructure
of surveillance authoritarianism
of surveillance authoritarianism
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gradimo ovu infrastrukturu
nadgledačkog autoritarizma
nadgledačkog autoritarizma
17:13
merely to get people to click on ads.
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samo kako bi ljudi kliknuli na oglase.
17:17
And this won't be
Orwell's authoritarianism.
Orwell's authoritarianism.
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Ovo neće biti
Orwellov autoritarizam.
Orwellov autoritarizam.
17:19
This isn't "1984."
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Ovo nije "1984".
17:21
Now, if authoritarianism
is using overt fear to terrorize us,
is using overt fear to terrorize us,
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Ako autoritarizam koristi
strah kako bi nas terorizirao,
strah kako bi nas terorizirao,
17:26
we'll all be scared, but we'll know it,
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svi ćemo biti u strahu, ali znat ćemo to,
17:29
we'll hate it and we'll resist it.
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mrzit ćemo to i opirat ćemo se tome.
17:32
But if the people in power
are using these algorithms
are using these algorithms
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Ali ako oni koji su na vlasti
koriste ove algoritme
koriste ove algoritme
17:37
to quietly watch us,
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kako bi nas tiho nadgledali,
17:40
to judge us and to nudge us,
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kako bi nas procjenjivali i gurkali,
17:43
to predict and identify
the troublemakers and the rebels,
the troublemakers and the rebels,
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kako bi predvidjeli i prepoznali
one koji prave probleme i buntovnike,
one koji prave probleme i buntovnike,
17:47
to deploy persuasion
architectures at scale
architectures at scale
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kako bi razvili
arhitekturu uvjeravanja u stvarnom životu
arhitekturu uvjeravanja u stvarnom životu
17:51
and to manipulate individuals one by one
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i kako bi manipulirali pojedincima
17:56
using their personal, individual
weaknesses and vulnerabilities,
weaknesses and vulnerabilities,
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koristeći osobne,
individualne slabosti i osjetljivosti,
individualne slabosti i osjetljivosti,
18:02
and if they're doing it at scale
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i ako to rade u velikom broju
18:06
through our private screens
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kroz naše privatne ekrane
18:07
so that we don't even know
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pa čak ni ne znamo
18:09
what our fellow citizens
and neighbors are seeing,
and neighbors are seeing,
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što naši sugrađani i susjedi vide,
18:13
that authoritarianism
will envelop us like a spider's web
will envelop us like a spider's web
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taj će se autoritarizam
omotati oko nas kao paukova mreža,
omotati oko nas kao paukova mreža,
18:18
and we may not even know we're in it.
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a mi nećemo ni znati da smo u njoj.
18:22
So Facebook's market capitalization
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Facebookova tržišna kapitalizacija
18:25
is approaching half a trillion dollars.
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iznosi pola trilijuna dolara
18:28
It's because it works great
as a persuasion architecture.
as a persuasion architecture.
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jer odlično funkcionira
kao arhitektura uvjeravanja.
kao arhitektura uvjeravanja.
18:33
But the structure of that architecture
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Ali struktura te arhitekture
18:36
is the same whether you're selling shoes
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ista je bez obzira
na to prodajete li cipele
na to prodajete li cipele
18:39
or whether you're selling politics.
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ili politiku.
18:42
The algorithms do not know the difference.
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Algoritmi tu ne vide razliku.
18:46
The same algorithms set loose upon us
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Ti isti algoritmi usmjereni na nas
18:49
to make us more pliable for ads
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kako bi nas napravili
podložnijima oglasima
podložnijima oglasima
18:52
are also organizing our political,
personal and social information flows,
personal and social information flows,
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također organiziraju naše političke,
osobne i društvene tokove informacija,
osobne i društvene tokove informacija,
18:59
and that's what's got to change.
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a to se mora promijeniti.
19:02
Now, don't get me wrong,
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Nemojte me krivo shvatiti,
19:04
we use digital platforms
because they provide us with great value.
because they provide us with great value.
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koristimo digitalne platforme
jer nam puno toga pružaju.
jer nam puno toga pružaju.
19:09
I use Facebook to keep in touch
with friends and family around the world.
with friends and family around the world.
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Koristim Face da budem u kontaktu
s prijateljima i obitelji diljem svijeta.
s prijateljima i obitelji diljem svijeta.
19:14
I've written about how crucial
social media is for social movements.
social media is for social movements.
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Pisala sam o tome koliko su ključni
društveni mediji za društvene pokrete.
društveni mediji za društvene pokrete.
19:19
I have studied how
these technologies can be used
these technologies can be used
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Proučavala sam kako se te
tehnologije mogu koristiti
tehnologije mogu koristiti
19:22
to circumvent censorship around the world.
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da zaobiđemo cenzuru diljem svijeta.
19:27
But it's not that the people who run,
you know, Facebook or Google
you know, Facebook or Google
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Ali ljudi koji vode Facebook ili Google
19:33
are maliciously and deliberately trying
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ne pokušavaju zlobno ili namjerno
19:36
to make the country
or the world more polarized
or the world more polarized
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državu ili svijet dodatno polarizirati
19:40
and encourage extremism.
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i poticati ekstremizam.
19:43
I read the many
well-intentioned statements
well-intentioned statements
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Pročitala sam mnoge dobronamjerne izjave
19:47
that these people put out.
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koje ti ljudi daju.
19:51
But it's not the intent or the statements
people in technology make that matter,
people in technology make that matter,
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No, nije važna namjera ili izjave
ljudi u tehnologiji,
ljudi u tehnologiji,
19:57
it's the structures
and business models they're building.
and business models they're building.
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već su važne strukture
i poslovni modeli koje oni grade.
i poslovni modeli koje oni grade.
20:02
And that's the core of the problem.
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To je srž problema.
20:04
Either Facebook is a giant con
of half a trillion dollars
of half a trillion dollars
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Ili je Facebook ogromni prevarant
s pola trilijuna dolara
s pola trilijuna dolara
20:10
and ads don't work on the site,
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i oglasi na toj stranici ne rade
20:12
it doesn't work
as a persuasion architecture,
as a persuasion architecture,
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kao arhitektura uvjeravanja,
20:14
or its power of influence
is of great concern.
is of great concern.
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ili je njegova moć utjecaja
razlog za brigu.
razlog za brigu.
20:20
It's either one or the other.
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Ili jedno ili drugo.
20:22
It's similar for Google, too.
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Slično vrijedi i za Google.
20:24
So what can we do?
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Što možemo učiniti?
20:27
This needs to change.
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To se mora promijeniti.
20:29
Now, I can't offer a simple recipe,
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Ne mogu ponuditi jednostavan recept
20:31
because we need to restructure
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jer moramo restrukturirati
20:34
the whole way our
digital technology operates.
digital technology operates.
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cjelokupan način na koji
naša digitalna tehnologija funkcionira.
naša digitalna tehnologija funkcionira.
20:37
Everything from the way
technology is developed
technology is developed
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Sve od toga kako se tehnologija razvija
20:41
to the way the incentives,
economic and otherwise,
economic and otherwise,
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pa do načina na koji su
poticaji, ekonomski ili drugi,
poticaji, ekonomski ili drugi,
20:45
are built into the system.
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ugrađeni u sustav.
20:48
We have to face and try to deal with
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Moramo se suočiti i pokušati nositi s
20:51
the lack of transparency
created by the proprietary algorithms,
created by the proprietary algorithms,
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manjkom transparentnosti
koju stvaraju privatni algoritmi,
koju stvaraju privatni algoritmi,
20:56
the structural challenge
of machine learning's opacity,
of machine learning's opacity,
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strukturalnim izazovom
nejasnoće strojnog učenja,
nejasnoće strojnog učenja,
21:00
all this indiscriminate data
that's being collected about us.
that's being collected about us.
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svim tim podacima
koji se o nama skupljaju.
koji se o nama skupljaju.
21:05
We have a big task in front of us.
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Pred nama je velik zadatak.
21:08
We have to mobilize our technology,
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Moramo mobilizirati našu tehnologiju,
21:11
our creativity
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našu kreativnost,
21:13
and yes, our politics
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i da, i našu politiku
21:16
so that we can build
artificial intelligence
artificial intelligence
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kako bismo stvorili umjetnu inteligenciju
21:18
that supports us in our human goals
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koja podržava naše ljudske ciljeve,
21:22
but that is also constrained
by our human values.
by our human values.
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ali koju ograničavaju
naše ljudske vrijednosti.
naše ljudske vrijednosti.
21:27
And I understand this won't be easy.
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Shvaćam da to neće biti lako.
21:30
We might not even easily agree
on what those terms mean.
on what those terms mean.
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Možda se nećemo čak ni složiti
o tome koji bi to uvjeti trebali biti.
o tome koji bi to uvjeti trebali biti.
21:34
But if we take seriously
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Ali ako ozbiljno shvatimo
21:38
how these systems that we
depend on for so much operate,
depend on for so much operate,
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kako funkcioniraju ovi sustavi
o kojima toliko dugo ovisimo,
o kojima toliko dugo ovisimo,
21:44
I don't see how we can postpone
this conversation anymore.
this conversation anymore.
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ne znam kako možemo
odgađati ovaj razgovor.
odgađati ovaj razgovor.
21:49
These structures
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Ove strukture
21:51
are organizing how we function
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organiziraju način na koji funkcioniramo
21:55
and they're controlling
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i kontroliraju
21:58
what we can and we cannot do.
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što možemo i što ne možemo raditi.
22:00
And many of these ad-financed platforms,
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Mnoge od tih platformi
koje financiraju oglasi
koje financiraju oglasi
22:03
they boast that they're free.
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naglašavaju da su besplatne.
22:04
In this context, it means
that we are the product that's being sold.
that we are the product that's being sold.
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U ovom kontekstu, to znači
da smo mi proizvod koji se prodaje.
da smo mi proizvod koji se prodaje.
22:10
We need a digital economy
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Potrebna nam je digitalna ekonomija
22:13
where our data and our attention
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gdje naši podaci i pozornost
22:17
is not for sale to the highest-bidding
authoritarian or demagogue.
authoritarian or demagogue.
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neće biti prodani diktatoru
ili demagogiji koja ponudi najviše.
ili demagogiji koja ponudi najviše.
22:23
(Applause)
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(Pljesak)
22:30
So to go back to
that Hollywood paraphrase,
that Hollywood paraphrase,
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Vratimo se onoj holivudskoj parafrazi,
22:33
we do want the prodigious potential
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želimo da ogroman potencijal
22:37
of artificial intelligence
and digital technology to blossom,
and digital technology to blossom,
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umjetne inteligencije
i digitalne tehnologije procvjeta,
i digitalne tehnologije procvjeta,
22:41
but for that, we must face
this prodigious menace,
this prodigious menace,
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ali zbog toga se moramo suočiti
s ovom ogromnom prijetnjom,
s ovom ogromnom prijetnjom,
22:46
open-eyed and now.
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otvorenih očiju, i to odmah.
22:48
Thank you.
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Hvala vam.
22:49
(Applause)
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(Pljesak)
ABOUT THE SPEAKER
Zeynep Tufekci - Techno-sociologistTechno-sociologist Zeynep Tufekci asks big questions about our societies and our lives, as both algorithms and digital connectivity spread.
Why you should listen
We've entered an era of digital connectivity and machine intelligence. Complex algorithms are increasingly used to make consequential decisions about us. Many of these decisions are subjective and have no right answer: who should be hired, fired or promoted; what news should be shown to whom; which of your friends do you see updates from; which convict should be paroled. With increasing use of machine learning in these systems, we often don't even understand how exactly they are making these decisions. Zeynep Tufekci studies what this historic transition means for culture, markets, politics and personal life.
Tufekci is a contributing opinion writer at the New York Times, an associate professor at the School of Information and Library Science at University of North Carolina, Chapel Hill, and a faculty associate at Harvard's Berkman Klein Center for Internet and Society.
Her book, Twitter and Tear Gas: The Power and Fragility of Networked Protest, was published in 2017 by Yale University Press. Her next book, from Penguin Random House, will be about algorithms that watch, judge and nudge us.
Zeynep Tufekci | Speaker | TED.com