TEDSummit
Zeynep Tufekci: Machine intelligence makes human morals more important
Zejnep Tufekči (Zeynep Tufekci): Zbog mašinske inteligencije ljudski moral je sve važniji
Filmed:
Readability: 4.9
1,648,711 views
Mašinska inteligencija je stigla, i već je koristimo u donošenju subjektivnih odluka. Međutim, zbog složenog načina rasta i napredovanja veštačke inteligencije teško ju je razumeti, a još teže kontrolisati. U ovom poučnom govoru, sociološkinja tehnologije Zejnep Tufekči objašnjava kako inteligentne mašine mogu da pogreše tako da se to ne uklapa u ljudske obrasce grešenja - kao i na neočekivane načine na koje nismo pripremljeni. "Ne možemo da delegiramo naša zaduženja mašinama", kaže. "Moramo se kao nikad pre čvrsto držati ljudskih vrednosti i ljudske etike."
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, I started my first job
as a computer programmer
as a computer programmer
0
739
4122
Dakle, počela sam da radim
kao kompjuterska programerka
kao kompjuterska programerka
00:16
in my very first year of college --
1
4885
1956
kad sam bila prva godina na fakultetu -
00:18
basically, as a teenager.
2
6865
1507
u suštini kao tinejdžerka.
00:20
Soon after I started working,
3
8889
1732
Ubrzo nakon što sam počela da radim,
00:22
writing software in a company,
4
10645
1610
da pišem softvere za firmu,
00:24
a manager who worked at the company
came down to where I was,
came down to where I was,
5
12799
3635
menadžer koji je radio u firmi
se spustio do mene
se spustio do mene
00:28
and he whispered to me,
6
16458
1268
i prošaputao mi je:
00:30
"Can he tell if I'm lying?"
7
18229
2861
"Zna li da lažem?"
00:33
There was nobody else in the room.
8
21806
2077
Nije bilo više bilo koga u prostoriji.
00:37
"Can who tell if you're lying?
And why are we whispering?"
And why are we whispering?"
9
25032
4389
"Zna li ko da lažete? I zašto šapućemo?"
00:42
The manager pointed
at the computer in the room.
at the computer in the room.
10
30266
3107
Menadžer je pokazao
na kompjuter u prostoriji.
na kompjuter u prostoriji.
00:45
"Can he tell if I'm lying?"
11
33397
3096
"Zna li da lažem?"
00:49
Well, that manager was having
an affair with the receptionist.
an affair with the receptionist.
12
37613
4362
Pa, taj menadžer je imao aferu
sa recepcionerom.
sa recepcionerom.
00:53
(Laughter)
13
41999
1112
(Smeh)
00:55
And I was still a teenager.
14
43135
1766
A ja sam još uvek bila tinejdžerka.
00:57
So I whisper-shouted back to him,
15
45447
2019
Pa sam mu polušapatom odgovorila:
00:59
"Yes, the computer can tell
if you're lying."
if you're lying."
16
47490
3624
"Da, kompjuter zna kad mu lažete."
01:03
(Laughter)
17
51138
1806
(Smeh)
01:04
Well, I laughed, but actually,
the laugh's on me.
the laugh's on me.
18
52968
2923
Pa, smejala sam se,
ali zapravo šala je na moj račun.
ali zapravo šala je na moj račun.
01:07
Nowadays, there are computational systems
19
55915
3268
Ovih dana imamo kompjuterske sisteme
01:11
that can suss out
emotional states and even lying
emotional states and even lying
20
59207
3548
koji mogu da prozru
emocionalna stanja, čak i laganje,
emocionalna stanja, čak i laganje,
01:14
from processing human faces.
21
62779
2044
obradom ljudskih lica.
01:17
Advertisers and even governments
are very interested.
are very interested.
22
65248
4153
Oglašivači, čak i vlade
su veoma zainteresovane za to.
su veoma zainteresovane za to.
01:22
I had become a computer programmer
23
70319
1862
Postala sam kompjuterska programerka
01:24
because I was one of those kids
crazy about math and science.
crazy about math and science.
24
72205
3113
jer sam bila jedno od dece
koja su luda za matematikom i naukom.
koja su luda za matematikom i naukom.
01:27
But somewhere along the line
I'd learned about nuclear weapons,
I'd learned about nuclear weapons,
25
75942
3108
No, nekako sam usput
saznala za nuklearno oružje
saznala za nuklearno oružje
01:31
and I'd gotten really concerned
with the ethics of science.
with the ethics of science.
26
79074
2952
i postala sam zaista zabrinuta
zbog naučne etike.
zbog naučne etike.
01:34
I was troubled.
27
82050
1204
Mučilo me je to.
01:35
However, because of family circumstances,
28
83278
2641
Međutim, zbog porodičnih okolnosti,
01:37
I also needed to start working
as soon as possible.
as soon as possible.
29
85943
3298
takođe je bilo potrebno
da počnem da radim što pre.
da počnem da radim što pre.
01:41
So I thought to myself, hey,
let me pick a technical field
let me pick a technical field
30
89265
3299
Pa sam pomislila u sebi, hej,
hajde da izaberem tehničku oblast
hajde da izaberem tehničku oblast
01:44
where I can get a job easily
31
92588
1796
gde mogu lako da se zaposlim
01:46
and where I don't have to deal
with any troublesome questions of ethics.
with any troublesome questions of ethics.
32
94408
4018
i gde ne moram da se bavim
bilo kakvim mučnim etičkim pitanjem.
bilo kakvim mučnim etičkim pitanjem.
01:51
So I picked computers.
33
99022
1529
Pa sam odabrala kompjutere.
01:52
(Laughter)
34
100575
1104
(Smeh)
01:53
Well, ha, ha, ha!
All the laughs are on me.
All the laughs are on me.
35
101703
3410
Pa, ha, ha, ha!
Šala je skroz na moj račun.
Šala je skroz na moj račun.
01:57
Nowadays, computer scientists
are building platforms
are building platforms
36
105137
2754
Ovih dana kompjuterski naučnici
prave platforme
prave platforme
01:59
that control what a billion
people see every day.
people see every day.
37
107915
4209
koje upravljaju onim
što milijarde ljudi gledaju svakodnevno.
što milijarde ljudi gledaju svakodnevno.
02:05
They're developing cars
that could decide who to run over.
that could decide who to run over.
38
113052
3822
Razvijaju automobile
koji mogu da odluče koga da pregaze.
koji mogu da odluče koga da pregaze.
02:09
They're even building machines, weapons,
39
117707
3213
Čak grade mašine, oružja,
02:12
that might kill human beings in war.
40
120944
2285
koja mogu da ubijaju ljude u ratu.
02:15
It's ethics all the way down.
41
123253
2771
U potpunosti se radi o etici.
02:19
Machine intelligence is here.
42
127183
2058
Mašinska inteligencija je tu.
02:21
We're now using computation
to make all sort of decisions,
to make all sort of decisions,
43
129823
3474
Trenutno koristimo kompjutere
da donesemo razne odluke,
da donesemo razne odluke,
02:25
but also new kinds of decisions.
44
133321
1886
ali i nove tipove odluka.
02:27
We're asking questions to computation
that have no single right answers,
that have no single right answers,
45
135231
5172
Postavljamo kompjuterima pitanja
koja nemaju jedan pravi odgovor,
koja nemaju jedan pravi odgovor,
02:32
that are subjective
46
140427
1202
koja su subjektivna,
02:33
and open-ended and value-laden.
47
141653
2325
otvorena i krcata vrednostima.
02:36
We're asking questions like,
48
144002
1758
Postavljamo ovakva pitanja:
02:37
"Who should the company hire?"
49
145784
1650
"Koga da firma zaposli?"
02:40
"Which update from which friend
should you be shown?"
should you be shown?"
50
148096
2759
"Koje ažuriranje od kog prijatelja
treba da bude vidljivo?"
treba da bude vidljivo?"
02:42
"Which convict is more
likely to reoffend?"
likely to reoffend?"
51
150879
2266
"Koji osuđenik je skloniji
novom prestupu?"
novom prestupu?"
02:45
"Which news item or movie
should be recommended to people?"
should be recommended to people?"
52
153514
3054
"Koji novinski čalanak ili film
treba preporučiti ljudima?"
treba preporučiti ljudima?"
02:48
Look, yes, we've been using
computers for a while,
computers for a while,
53
156592
3372
Gledajte, da, već neko vreme
koristimo kompjutere,
koristimo kompjutere,
02:51
but this is different.
54
159988
1517
ali ovo je drugačije.
02:53
This is a historical twist,
55
161529
2067
Ovo je istorijski preokret
02:55
because we cannot anchor computation
for such subjective decisions
for such subjective decisions
56
163620
5337
jer ne možemo da usidrimo kompjutere
kod sličnih subjektivnih odluka
kod sličnih subjektivnih odluka
03:00
the way we can anchor computation
for flying airplanes, building bridges,
for flying airplanes, building bridges,
57
168981
5420
na način na koji usidravamo kompjutere
koji upravljaju avionima, grade mostove,
koji upravljaju avionima, grade mostove,
03:06
going to the moon.
58
174425
1259
idu na mesec.
03:08
Are airplanes safer?
Did the bridge sway and fall?
Did the bridge sway and fall?
59
176449
3259
Jesu li avioni bezbedniji?
Da li se most zaljuljao i pao?
Da li se most zaljuljao i pao?
03:11
There, we have agreed-upon,
fairly clear benchmarks,
fairly clear benchmarks,
60
179732
4498
Tu imamo uspostavljene
prilično jasne repere
prilično jasne repere
03:16
and we have laws of nature to guide us.
61
184254
2239
i tu su zakoni prirode da nas vode.
03:18
We have no such anchors and benchmarks
62
186517
3394
Nemamo slična težišta i repere
03:21
for decisions in messy human affairs.
63
189935
3963
za odluke koje se tiču
haotičnih ljudskih odnosa.
haotičnih ljudskih odnosa.
03:25
To make things more complicated,
our software is getting more powerful,
our software is getting more powerful,
64
193922
4237
Da bi stvari bile još složenije,
naši softveri postaju sve moćniji,
naši softveri postaju sve moćniji,
03:30
but it's also getting less
transparent and more complex.
transparent and more complex.
65
198183
3773
ali takođe postaju manje
transparentni i složeniji.
transparentni i složeniji.
03:34
Recently, in the past decade,
66
202542
2040
U skorije vreme, u poslednjoj deceniji,
03:36
complex algorithms
have made great strides.
have made great strides.
67
204606
2729
složeni algoritmi
su poprilično napredovali.
su poprilično napredovali.
03:39
They can recognize human faces.
68
207359
1990
Mogu da prepoznaju ljudska lica.
03:41
They can decipher handwriting.
69
209985
2055
Mogu da dešifruju rukopis.
03:44
They can detect credit card fraud
70
212436
2066
Mogu da zapaze prevaru
kod kreditnih kartica
kod kreditnih kartica
03:46
and block spam
71
214526
1189
i da blokiraju spamove
03:47
and they can translate between languages.
72
215739
2037
i da prevode s jezika na jezik.
03:49
They can detect tumors in medical imaging.
73
217800
2574
Mogu da zapaze tumore
na medicinskim snimcima.
na medicinskim snimcima.
03:52
They can beat humans in chess and Go.
74
220398
2205
Mogu da pobede ljude u šahu i gou.
03:55
Much of this progress comes
from a method called "machine learning."
from a method called "machine learning."
75
223264
4504
Veliki deo ovog napretka potiče
od metoda nazvanog "mašinsko učenje".
od metoda nazvanog "mašinsko učenje".
04:00
Machine learning is different
than traditional programming,
than traditional programming,
76
228175
3187
Mašinsko učenje se razlikuje
od tradicionalnog programiranja,
od tradicionalnog programiranja,
04:03
where you give the computer
detailed, exact, painstaking instructions.
detailed, exact, painstaking instructions.
77
231386
3585
gde dajete kompjuteru
detaljne, tačne, minuciozne instrukcije.
detaljne, tačne, minuciozne instrukcije.
04:07
It's more like you take the system
and you feed it lots of data,
and you feed it lots of data,
78
235378
4182
Pre se radi o odabiru sistema
i pohranjivanju podataka u njega,
i pohranjivanju podataka u njega,
uključujući nestrukturirane podatke,
04:11
including unstructured data,
79
239584
1656
04:13
like the kind we generate
in our digital lives.
in our digital lives.
80
241264
2278
poput onih koje stvaramo
u digitalnim životima.
u digitalnim životima.
04:15
And the system learns
by churning through this data.
by churning through this data.
81
243566
2730
A sistem uči, pretresajući podatke.
04:18
And also, crucially,
82
246669
1526
Suštinsko je takođe
04:20
these systems don't operate
under a single-answer logic.
under a single-answer logic.
83
248219
4380
da se ovi sistemi ne vode
logikom samo jednog odgovora.
logikom samo jednog odgovora.
04:24
They don't produce a simple answer;
it's more probabilistic:
it's more probabilistic:
84
252623
2959
Ne proizvode jednostavne odgovore;
više se radi o verovatnoći:
više se radi o verovatnoći:
04:27
"This one is probably more like
what you're looking for."
what you're looking for."
85
255606
3483
"Ovo je verovatno sličnije
onome što tražite."
onome što tražite."
04:32
Now, the upside is:
this method is really powerful.
this method is really powerful.
86
260023
3070
Sad, pozitivno je:
ovaj metod je zaista moćan.
ovaj metod je zaista moćan.
Glavni u Guglovom sistemu
za VI je to nazvao:
za VI je to nazvao:
04:35
The head of Google's AI systems called it,
87
263117
2076
04:37
"the unreasonable effectiveness of data."
88
265217
2197
"nerazumna efikasnost podataka."
04:39
The downside is,
89
267791
1353
Negativno je:
04:41
we don't really understand
what the system learned.
what the system learned.
90
269738
3071
ne razumemo zaista šta je sistem naučio.
04:44
In fact, that's its power.
91
272833
1587
Zapravo, to je njegova moć.
04:46
This is less like giving
instructions to a computer;
instructions to a computer;
92
274946
3798
Ovo manje liči na davanje
uputstava kompjuteru;
uputstava kompjuteru;
04:51
it's more like training
a puppy-machine-creature
a puppy-machine-creature
93
279200
4064
više liči na dresiranje
bića - mehaničko kuče,
bića - mehaničko kuče,
04:55
we don't really understand or control.
94
283288
2371
koje zaista ne razumemo,
niti kontrolišemo.
niti kontrolišemo.
04:58
So this is our problem.
95
286362
1551
Dakle, to je naš problem.
05:00
It's a problem when this artificial
intelligence system gets things wrong.
intelligence system gets things wrong.
96
288427
4262
Problem je kad ovaj sistem veštačke
inteligencije nešto pogrešno shvati.
inteligencije nešto pogrešno shvati.
05:04
It's also a problem
when it gets things right,
when it gets things right,
97
292713
3540
Takođe je problem kad nešto dobro shvati.
05:08
because we don't even know which is which
when it's a subjective problem.
when it's a subjective problem.
98
296277
3628
jer čak ni ne znamo šta je šta
kod subjektivnog problema.
kod subjektivnog problema.
05:11
We don't know what this thing is thinking.
99
299929
2339
Ne znamo o čemu ova stvar razmišlja.
05:15
So, consider a hiring algorithm --
100
303493
3683
Dakle, uzmite u obzir
algoritam za zapošljavanje -
algoritam za zapošljavanje -
05:20
a system used to hire people,
using machine-learning systems.
using machine-learning systems.
101
308123
4311
sistem koji se koristi pri zapošljavanju,
koji koristi sisteme mašinskog učenja.
koji koristi sisteme mašinskog učenja.
05:25
Such a system would have been trained
on previous employees' data
on previous employees' data
102
313052
3579
Sličan sistem je obučavan
na podacima prethodnih zaposlenih
na podacima prethodnih zaposlenih
05:28
and instructed to find and hire
103
316655
2591
i naučen je da pronalazi i zapošljava
05:31
people like the existing
high performers in the company.
high performers in the company.
104
319270
3038
ljude poput postojećih
najučinkovitijih u firmi.
najučinkovitijih u firmi.
05:34
Sounds good.
105
322814
1153
Zvuči dobro.
05:35
I once attended a conference
106
323991
1999
Jednom sam bila na konferenciji
05:38
that brought together
human resources managers and executives,
human resources managers and executives,
107
326014
3125
koja je spojila menadžere
iz kadrovske službe i direktore,
iz kadrovske službe i direktore,
ljude s visokih pozicija,
05:41
high-level people,
108
329163
1206
koristeći ove sisteme zapošljavanja.
05:42
using such systems in hiring.
109
330393
1559
05:43
They were super excited.
110
331976
1646
Bili su veoma uzbuđeni.
05:45
They thought that this would make hiring
more objective, less biased,
more objective, less biased,
111
333646
4653
Smatrali su da bi zbog ovoga zapošljavanje
bilo objektivnije, nepristrasnije,
bilo objektivnije, nepristrasnije,
05:50
and give women
and minorities a better shot
and minorities a better shot
112
338323
3000
i da bi žene i manjine imale više šanse,
05:53
against biased human managers.
113
341347
2188
nasuprot pristrasnim ljudskim menadžerima.
05:55
And look -- human hiring is biased.
114
343559
2843
I, gledajte -
zapošljavanje ljudi je pristrasno.
zapošljavanje ljudi je pristrasno.
05:59
I know.
115
347099
1185
Znam.
06:00
I mean, in one of my early jobs
as a programmer,
as a programmer,
116
348308
3005
Mislim, na jednom od mojih
prvih poslova kao programerke,
prvih poslova kao programerke,
06:03
my immediate manager would sometimes
come down to where I was
come down to where I was
117
351337
3868
moja nadređena menadžerka bi ponekad
prišla mestu na kom sam,
prišla mestu na kom sam,
06:07
really early in the morning
or really late in the afternoon,
or really late in the afternoon,
118
355229
3753
veoma rano ujutru
ili veoma kasno poslepodne,
ili veoma kasno poslepodne,
06:11
and she'd say, "Zeynep,
let's go to lunch!"
let's go to lunch!"
119
359006
3062
i rekla bi: "Zejnep, pođimo na ručak!"
06:14
I'd be puzzled by the weird timing.
120
362724
2167
Zbunilo bi me neobično vreme.
06:16
It's 4pm. Lunch?
121
364915
2129
Četiri je popodne. Ručak?
06:19
I was broke, so free lunch. I always went.
122
367068
3094
Bila sam švorc, pa sam uvek išla
na besplatan ručak.
na besplatan ručak.
06:22
I later realized what was happening.
123
370618
2067
Kasnije sam shvatila o čemu se radilo.
06:24
My immediate managers
had not confessed to their higher-ups
had not confessed to their higher-ups
124
372709
4546
Moji nadređeni menadžeri
nisu priznali svojim nadređenim
nisu priznali svojim nadređenim
da je programer kog su zaposlili
za ozbiljan posao bila tinejdžerka
za ozbiljan posao bila tinejdžerka
06:29
that the programmer they hired
for a serious job was a teen girl
for a serious job was a teen girl
125
377279
3113
06:32
who wore jeans and sneakers to work.
126
380416
3930
koja je nosila farmerke i patike na posao.
06:37
I was doing a good job,
I just looked wrong
I just looked wrong
127
385174
2202
Bila sam dobar radnik,
samo pogrešnog izgleda
samo pogrešnog izgleda
06:39
and was the wrong age and gender.
128
387400
1699
i bila sam pogrešnih godina i roda.
06:41
So hiring in a gender- and race-blind way
129
389123
3346
Pa zapošljavanje
na rodno i rasno nepristrasan način
na rodno i rasno nepristrasan način
06:44
certainly sounds good to me.
130
392493
1865
izvesno da mi zvuči dobro.
06:47
But with these systems,
it is more complicated, and here's why:
it is more complicated, and here's why:
131
395031
3341
Ali uz ove sisteme,
složenije je, a evo zašto:
složenije je, a evo zašto:
06:50
Currently, computational systems
can infer all sorts of things about you
can infer all sorts of things about you
132
398968
5791
trenutno kompjuterski sistemi
mogu da zaključe razne stvari o vama
mogu da zaključe razne stvari o vama
06:56
from your digital crumbs,
133
404783
1872
iz vaših digitalnih tragova,
06:58
even if you have not
disclosed those things.
disclosed those things.
134
406679
2333
čak iako to niste obelodanili.
07:01
They can infer your sexual orientation,
135
409506
2927
Mogu da zaključe vašu
seksualnu orijentaciju,
seksualnu orijentaciju,
07:04
your personality traits,
136
412994
1306
vaše lične osobine,
07:06
your political leanings.
137
414859
1373
vaša politička naginjanja.
07:08
They have predictive power
with high levels of accuracy.
with high levels of accuracy.
138
416830
3685
Imaju moć predviđanja
sa visokim stepenom tačnosti.
sa visokim stepenom tačnosti.
07:13
Remember -- for things
you haven't even disclosed.
you haven't even disclosed.
139
421362
2578
Zapamtite - za ono što čak
niste ni obelodanili.
niste ni obelodanili.
07:15
This is inference.
140
423964
1591
To je zaključivanje.
07:17
I have a friend who developed
such computational systems
such computational systems
141
425579
3261
Imam prijateljicu koja je razvila
sličan kompjuterski sistem
sličan kompjuterski sistem
07:20
to predict the likelihood
of clinical or postpartum depression
of clinical or postpartum depression
142
428864
3641
za predviđanje verovatnoće
kliničke ili postporođajne depresije
kliničke ili postporođajne depresije
07:24
from social media data.
143
432529
1416
iz podataka sa društvenih mreža.
07:26
The results are impressive.
144
434676
1427
Rezultati su bili impresivni.
07:28
Her system can predict
the likelihood of depression
the likelihood of depression
145
436492
3357
Njeni sistemi mogu da predvide
verovatnoću depresije
verovatnoću depresije
07:31
months before the onset of any symptoms --
146
439873
3903
mesecima pre nastupa
bilo kakvih simptoma -
bilo kakvih simptoma -
07:35
months before.
147
443800
1373
mesecima ranije.
07:37
No symptoms, there's prediction.
148
445197
2246
Bez simptoma imamo predviđanje.
07:39
She hopes it will be used
for early intervention. Great!
for early intervention. Great!
149
447467
4812
Ona se nada da će biti korišćeni
za rane intervencije. Sjajno!
za rane intervencije. Sjajno!
07:44
But now put this in the context of hiring.
150
452911
2040
Sad ovo stavite u kontekst zapošljavanja.
07:48
So at this human resources
managers conference,
managers conference,
151
456027
3046
Pa sam na ovoj konferenciji
menadžera iz kadrovske
menadžera iz kadrovske
07:51
I approached a high-level manager
in a very large company,
in a very large company,
152
459097
4709
prišla visokoprofilnoj menadžerki
iz prilično velike firme,
iz prilično velike firme,
07:55
and I said to her, "Look,
what if, unbeknownst to you,
what if, unbeknownst to you,
153
463830
4578
i rekla sam joj: "Pazi, šta ako bi,
bez tvog znanja,
bez tvog znanja,
08:00
your system is weeding out people
with high future likelihood of depression?
with high future likelihood of depression?
154
468432
6549
ovaj sistem iskorenjivao ljude sa velikim
izgledima za depresiju u budućnosti?
izgledima za depresiju u budućnosti?
08:07
They're not depressed now,
just maybe in the future, more likely.
just maybe in the future, more likely.
155
475761
3376
Trenutno nisu depresivni, ali je veća
verovatnoća da će biti u budućnosti.
verovatnoća da će biti u budućnosti.
08:11
What if it's weeding out women
more likely to be pregnant
more likely to be pregnant
156
479923
3406
Šta ako iskorenjuje žene
s većom verovatnoćom da zatrudne
s većom verovatnoćom da zatrudne
08:15
in the next year or two
but aren't pregnant now?
but aren't pregnant now?
157
483353
2586
u narednih godinu ili dve,
ali trenutno nisu trudne?
ali trenutno nisu trudne?
08:18
What if it's hiring aggressive people
because that's your workplace culture?"
because that's your workplace culture?"
158
486844
5636
Šta ako zapošljava agresivne ljude
jer je to kultura na vašem radnom mestu?"
jer je to kultura na vašem radnom mestu?"
08:25
You can't tell this by looking
at gender breakdowns.
at gender breakdowns.
159
493173
2691
Ovo ne možete da vidite,
posmatrajući rodnu nejednakost.
posmatrajući rodnu nejednakost.
08:27
Those may be balanced.
160
495888
1502
Ona bi mogla da bude u ravnoteži.
08:29
And since this is machine learning,
not traditional coding,
not traditional coding,
161
497414
3557
A kako se radi o mašinskom učenju,
a ne tradicionalnom programiranju,
a ne tradicionalnom programiranju,
08:32
there is no variable there
labeled "higher risk of depression,"
labeled "higher risk of depression,"
162
500995
4907
tu nemamo varijablu
s oznakom "veći rizik od depresije",
s oznakom "veći rizik od depresije",
08:37
"higher risk of pregnancy,"
163
505926
1833
"veći rizik za trudnoću",
08:39
"aggressive guy scale."
164
507783
1734
"skala agresivnih muškaraca".
08:41
Not only do you not know
what your system is selecting on,
what your system is selecting on,
165
509995
3679
Ne samo da ne znate
na osnovu čega vaš sistem bira,
na osnovu čega vaš sistem bira,
08:45
you don't even know
where to begin to look.
where to begin to look.
166
513698
2323
čak ne znate ni gde da gledate.
08:48
It's a black box.
167
516045
1246
To je crna kutija.
08:49
It has predictive power,
but you don't understand it.
but you don't understand it.
168
517315
2807
Ima moć predviđanja,
ali je vi ne razumete.
ali je vi ne razumete.
08:52
"What safeguards," I asked, "do you have
169
520486
2369
"Koja vam je zaštita",
pitala sam, "koju imate
pitala sam, "koju imate
08:54
to make sure that your black box
isn't doing something shady?"
isn't doing something shady?"
170
522879
3673
kojom se starate da crna kutija
ne obavlja nešto sumnjivo?"
ne obavlja nešto sumnjivo?"
09:00
She looked at me as if I had
just stepped on 10 puppy tails.
just stepped on 10 puppy tails.
171
528863
3878
Pogledala me je kao da sam
nagazila na 10 kučećih repića.
nagazila na 10 kučećih repića.
09:04
(Laughter)
172
532765
1248
(Smeh)
09:06
She stared at me and she said,
173
534037
2041
Buljila je u mene i rekla:
09:08
"I don't want to hear
another word about this."
another word about this."
174
536556
4333
"Ne želim da čujem ni reč više o ovome."
09:13
And she turned around and walked away.
175
541458
2034
Okrenula se i otišla.
09:16
Mind you -- she wasn't rude.
176
544064
1486
Pazite - nije bila nepristojna.
09:17
It was clearly: what I don't know
isn't my problem, go away, death stare.
isn't my problem, go away, death stare.
177
545574
6308
Jasno se radilo o ovome: ono što ne znam
nije moj problem, nestani, prazan pogeld.
nije moj problem, nestani, prazan pogeld.
09:23
(Laughter)
178
551906
1246
(Smeh)
09:25
Look, such a system
may even be less biased
may even be less biased
179
553862
3839
Vidite, sličan sistem bi mogao
čak da bude na neki način
čak da bude na neki način
09:29
than human managers in some ways.
180
557725
2103
i manje pristrasan od ljudskih menadžera.
09:31
And it could make monetary sense.
181
559852
2146
I mogao bi da ima finansijsku prednost.
09:34
But it could also lead
182
562573
1650
Ali bi takođe mogao da dovede
09:36
to a steady but stealthy
shutting out of the job market
shutting out of the job market
183
564247
4748
do stabilnog, ali prikrivenog
isključivanja sa tržišta rada
isključivanja sa tržišta rada
09:41
of people with higher risk of depression.
184
569019
2293
ljudi s većim rizikom od depresije.
09:43
Is this the kind of society
we want to build,
we want to build,
185
571753
2596
Da li je ovo oblik društva
koji želimo da gradimo,
koji želimo da gradimo,
09:46
without even knowing we've done this,
186
574373
2285
a da pri tom ne znamo da smo to uradili
09:48
because we turned decision-making
to machines we don't totally understand?
to machines we don't totally understand?
187
576682
3964
jer smo prepustili donošenje odluka
mašinama koje u potpunosti ne razumemo?
mašinama koje u potpunosti ne razumemo?
09:53
Another problem is this:
188
581265
1458
Drugi problem je sledeće:
09:55
these systems are often trained
on data generated by our actions,
on data generated by our actions,
189
583314
4452
ovi sistemi su često obučavani
na podacima koje proizvode naša delanja,
na podacima koje proizvode naša delanja,
09:59
human imprints.
190
587790
1816
na ljudskom otisku.
10:02
Well, they could just be
reflecting our biases,
reflecting our biases,
191
590188
3808
Pa, oni bi prosto mogli
da odražavaju naše predrasude,
da odražavaju naše predrasude,
10:06
and these systems
could be picking up on our biases
could be picking up on our biases
192
594020
3593
i ovi sistemi bi mogli
da pokupe naše predrasude
da pokupe naše predrasude
10:09
and amplifying them
193
597637
1313
i da ih naglase
10:10
and showing them back to us,
194
598974
1418
i potom da nam ih pokažu,
10:12
while we're telling ourselves,
195
600416
1462
dok mi govorimo sebi:
10:13
"We're just doing objective,
neutral computation."
neutral computation."
196
601902
3117
"Samo izvodimo objektivne,
neutralne proračune."
neutralne proračune."
10:18
Researchers found that on Google,
197
606314
2677
Istraživači su otkrili da na Guglu
10:22
women are less likely than men
to be shown job ads for high-paying jobs.
to be shown job ads for high-paying jobs.
198
610134
5313
ženama mnogo ređe nego muškarcima
prikazuju oglase za dobro plaćene poslove.
prikazuju oglase za dobro plaćene poslove.
10:28
And searching for African-American names
199
616463
2530
A pretraga afroameričkih imena
10:31
is more likely to bring up ads
suggesting criminal history,
suggesting criminal history,
200
619017
4706
često sa sobom povlači oglase
koji nagoveštavaju kriminalnu prošlost,
koji nagoveštavaju kriminalnu prošlost,
10:35
even when there is none.
201
623747
1567
čak i kad ona ne postoji.
10:38
Such hidden biases
and black-box algorithms
and black-box algorithms
202
626693
3549
Slične prikrivene predrasude
i algoritmi nalik crnoj kutiji,
i algoritmi nalik crnoj kutiji,
10:42
that researchers uncover sometimes
but sometimes we don't know,
but sometimes we don't know,
203
630266
3973
koje istraživači povremeno otkrivaju,
ali ponekad to ne uspeju,
ali ponekad to ne uspeju,
10:46
can have life-altering consequences.
204
634263
2661
mogu da imaju ozbiljne posledice.
10:49
In Wisconsin, a defendant
was sentenced to six years in prison
was sentenced to six years in prison
205
637958
4159
Okrivljeni iz Viskonsina
je osuđen na šest godina zatvora
je osuđen na šest godina zatvora
10:54
for evading the police.
206
642141
1355
zbog izbegavanja policije.
10:56
You may not know this,
207
644824
1186
Možda ne znate za ovo,
10:58
but algorithms are increasingly used
in parole and sentencing decisions.
in parole and sentencing decisions.
208
646034
3998
ali algoritme sve češće koriste
u odlučivanju o uslovnoj ili kazni.
u odlučivanju o uslovnoj ili kazni.
11:02
He wanted to know:
How is this score calculated?
How is this score calculated?
209
650056
2955
Želeo je da zna:
kako su izračunali ovaj rezultat?
kako su izračunali ovaj rezultat?
11:05
It's a commercial black box.
210
653795
1665
To je komercijalna crna kutija.
11:07
The company refused to have its algorithm
be challenged in open court.
be challenged in open court.
211
655484
4205
Firma je odbila da njen algoritam
izazovu na javnom suđenju.
izazovu na javnom suđenju.
11:12
But ProPublica, an investigative
nonprofit, audited that very algorithm
nonprofit, audited that very algorithm
212
660396
5532
No, ProPublica, istraživačka neprofitna
organizacija je proverila taj algoritam
organizacija je proverila taj algoritam
11:17
with what public data they could find,
213
665952
2016
sa podacima koje su uspeli da nađu
11:19
and found that its outcomes were biased
214
667992
2316
i otkrili su da su njihovi
rezultati pristrasni,
rezultati pristrasni,
11:22
and its predictive power
was dismal, barely better than chance,
was dismal, barely better than chance,
215
670332
3629
a da je njihova moć predviđanja očajna,
jedva bolja od nagađanja
jedva bolja od nagađanja
11:25
and it was wrongly labeling
black defendants as future criminals
black defendants as future criminals
216
673985
4416
i da su pogrešno označavali
okrivljene crnce kao buduće kriminalce,
okrivljene crnce kao buduće kriminalce,
11:30
at twice the rate of white defendants.
217
678425
3895
dvostruko češće nego okrivljene belce.
11:35
So, consider this case:
218
683891
1564
Pa, razmotrite ovaj slučaj:
11:38
This woman was late
picking up her godsister
picking up her godsister
219
686103
3852
ova žena je kasnila da pokupi svoje kumče
11:41
from a school in Broward County, Florida,
220
689979
2075
iz okruga Brauard u Floridi,
11:44
running down the street
with a friend of hers.
with a friend of hers.
221
692757
2356
trčala je niz ulicu
sa svojom prijateljicom.
sa svojom prijateljicom.
11:47
They spotted an unlocked kid's bike
and a scooter on a porch
and a scooter on a porch
222
695137
4099
Spazile su nezaključan dečji bicikl
i skuter na tremu
i skuter na tremu
11:51
and foolishly jumped on it.
223
699260
1632
i nesmotreno su sele na bicikl.
11:52
As they were speeding off,
a woman came out and said,
a woman came out and said,
224
700916
2599
Dok su jurile dalje,
žena je izašla i rekla:
žena je izašla i rekla:
11:55
"Hey! That's my kid's bike!"
225
703539
2205
"Hej! To je bicikl mog deteta!"
11:57
They dropped it, they walked away,
but they were arrested.
but they were arrested.
226
705768
3294
Ostavile su ga, odšetale, ali su uhapšene.
12:01
She was wrong, she was foolish,
but she was also just 18.
but she was also just 18.
227
709086
3637
Pogrešila je, bila je nesmotrena,
ali je takođe imala svega 18 godina.
ali je takođe imala svega 18 godina.
12:04
She had a couple of juvenile misdemeanors.
228
712747
2544
Imala je nekoliko maloletničkih prekršaja.
12:07
Meanwhile, that man had been arrested
for shoplifting in Home Depot --
for shoplifting in Home Depot --
229
715808
5185
U međuvremenu, ovaj čovek je uhapšen
zbog krađe u supermarketu -
zbog krađe u supermarketu -
12:13
85 dollars' worth of stuff,
a similar petty crime.
a similar petty crime.
230
721017
2924
robe u vrednosti od 85 dolara,
sličan manji zločin.
sličan manji zločin.
12:16
But he had two prior
armed robbery convictions.
armed robbery convictions.
231
724766
4559
Ali je pre toga imao
dve osude zbog oružane pljačke.
dve osude zbog oružane pljačke.
12:21
But the algorithm scored her
as high risk, and not him.
as high risk, and not him.
232
729955
3482
Ali je algoritam nju ocenio
kao visokorizičnu, a njega nije.
kao visokorizičnu, a njega nije.
12:26
Two years later, ProPublica found
that she had not reoffended.
that she had not reoffended.
233
734746
3874
Dve godine kasnije, ProPublica je otkrila
da ona nije imala novih prekršaja.
da ona nije imala novih prekršaja.
12:30
It was just hard to get a job
for her with her record.
for her with her record.
234
738644
2550
Samo joj je sa dosijeom
bilo teško da nađe posao.
bilo teško da nađe posao.
12:33
He, on the other hand, did reoffend
235
741218
2076
On, s druge strane, ponovo je u zatvoru
12:35
and is now serving an eight-year
prison term for a later crime.
prison term for a later crime.
236
743318
3836
i ovaj put služi kaznu od osam godina
zbog kasnijeg zločina.
zbog kasnijeg zločina.
12:40
Clearly, we need to audit our black boxes
237
748088
3369
Očigledno, moramo da proveravamo
naše crne kutije
naše crne kutije
12:43
and not have them have
this kind of unchecked power.
this kind of unchecked power.
238
751481
2615
kako ne bi imale
sličnu nekontrolisanu moć.
sličnu nekontrolisanu moć.
12:46
(Applause)
239
754120
2879
(Aplauz)
12:50
Audits are great and important,
but they don't solve all our problems.
but they don't solve all our problems.
240
758087
4242
Provere su sjajne i važne,
ali ne rešavaju sve naše probleme.
ali ne rešavaju sve naše probleme.
12:54
Take Facebook's powerful
news feed algorithm --
news feed algorithm --
241
762353
2748
Uzmite Fejsbukov moćan
algoritam za dostavu vesti -
algoritam za dostavu vesti -
12:57
you know, the one that ranks everything
and decides what to show you
and decides what to show you
242
765125
4843
znate, onaj koji sve rangira
i odlučuje šta da vam pokaže
i odlučuje šta da vam pokaže
13:01
from all the friends and pages you follow.
243
769992
2284
od svih prijatelja
i stranica koje pratite.
i stranica koje pratite.
13:04
Should you be shown another baby picture?
244
772898
2275
Da li da vam pokaže još jednu sliku bebe?
13:07
(Laughter)
245
775197
1196
(Smeh)
13:08
A sullen note from an acquaintance?
246
776417
2596
Sumornu poruku od poznanika?
13:11
An important but difficult news item?
247
779449
1856
Važnu, ali tešku vest?
13:13
There's no right answer.
248
781329
1482
Nema pravog odgovora.
13:14
Facebook optimizes
for engagement on the site:
for engagement on the site:
249
782835
2659
Fejsbuk najviše ima koristi
od angažmana na sajtu:
od angažmana na sajtu:
13:17
likes, shares, comments.
250
785518
1415
sviđanja, deljenja, komentara.
13:20
In August of 2014,
251
788168
2696
Avgusta 2014,
13:22
protests broke out in Ferguson, Missouri,
252
790888
2662
izbili su protesti u Fergusonu, Misuri,
13:25
after the killing of an African-American
teenager by a white police officer,
teenager by a white police officer,
253
793574
4417
nakon ubistva afroameričkog tinejdžera
od strane policajca belca,
od strane policajca belca,
13:30
under murky circumstances.
254
798015
1570
pod nejasnim okolnostima.
13:31
The news of the protests was all over
255
799974
2007
Vesti o protestima su bile svuda
13:34
my algorithmically
unfiltered Twitter feed,
unfiltered Twitter feed,
256
802005
2685
po mom algoritamski nefilterisanom
Tviter nalogu,
Tviter nalogu,
13:36
but nowhere on my Facebook.
257
804714
1950
ali nigde na mom Fejsbuku.
Da li su krivi prijatelji na Fejsbuku?
13:39
Was it my Facebook friends?
258
807182
1734
13:40
I disabled Facebook's algorithm,
259
808940
2032
Onemogućila sam Fejsbukov algoritam,
13:43
which is hard because Facebook
keeps wanting to make you
keeps wanting to make you
260
811472
2848
a to je teško jer Fejsbuk
nastoji da vas natera
nastoji da vas natera
13:46
come under the algorithm's control,
261
814344
2036
da budete pod kontrolom algoritma,
13:48
and saw that my friends
were talking about it.
were talking about it.
262
816404
2238
i videla sam da moji prijatelji
govore o tome.
govore o tome.
13:50
It's just that the algorithm
wasn't showing it to me.
wasn't showing it to me.
263
818666
2509
Samo mi moj algoritam to nije pokazivao.
13:53
I researched this and found
this was a widespread problem.
this was a widespread problem.
264
821199
3042
Istražila sam ovo i otkrila
da je ovo raširen problem.
da je ovo raširen problem.
13:56
The story of Ferguson
wasn't algorithm-friendly.
wasn't algorithm-friendly.
265
824265
3813
Priča o Fergusonu
nije bila prihvatljiva za algoritam.
nije bila prihvatljiva za algoritam.
Nije nešto za "sviđanje".
14:00
It's not "likable."
266
828102
1171
14:01
Who's going to click on "like?"
267
829297
1552
Ko će da pritisne "sviđanje"?
14:03
It's not even easy to comment on.
268
831500
2206
Nije je čak lako ni komentarisati.
14:05
Without likes and comments,
269
833730
1371
Bez sviđanja i komentara,
14:07
the algorithm was likely showing it
to even fewer people,
to even fewer people,
270
835125
3292
algoritam je težio da je prikaže
čak i manjem broju ljudi,
čak i manjem broju ljudi,
14:10
so we didn't get to see this.
271
838441
1542
pa nismo mogli da je vidimo.
14:12
Instead, that week,
272
840946
1228
Umesto toga, te sedmice,
14:14
Facebook's algorithm highlighted this,
273
842198
2298
Fejsbukov algoritam je izdvojio ovo,
14:16
which is the ALS Ice Bucket Challenge.
274
844520
2226
a to je ALS ledeni izazov.
14:18
Worthy cause; dump ice water,
donate to charity, fine.
donate to charity, fine.
275
846770
3742
Plemenit cilj; polij se ledenom vodom,
doniraj u dobrotvorne svrhe, fino.
doniraj u dobrotvorne svrhe, fino.
14:22
But it was super algorithm-friendly.
276
850536
1904
Ali bilo je veoma
algoritamski prihvatljivo.
algoritamski prihvatljivo.
14:25
The machine made this decision for us.
277
853219
2613
Mašina je donela ovu odluku u naše ime.
14:27
A very important
but difficult conversation
but difficult conversation
278
855856
3497
Veoma važan, ali težak razgovor
14:31
might have been smothered,
279
859377
1555
je mogao da bude ugušen,
14:32
had Facebook been the only channel.
280
860956
2696
da je Fejsbuk bio jedini kanal.
14:36
Now, finally, these systems
can also be wrong
can also be wrong
281
864117
3797
Sad, naposletku, ovi sistemi
takođe mogu da greše
takođe mogu da greše
14:39
in ways that don't resemble human systems.
282
867938
2736
drugačije od ljudskih sistema.
Ljudi, sećate li se Votsona,
IBM-ovog sistema mašinske inteligencije
IBM-ovog sistema mašinske inteligencije
14:42
Do you guys remember Watson,
IBM's machine-intelligence system
IBM's machine-intelligence system
283
870698
2922
14:45
that wiped the floor
with human contestants on Jeopardy?
with human contestants on Jeopardy?
284
873644
3128
koji je obrisao pod
ljudskim takmičarima na kvizu?
ljudskim takmičarima na kvizu?
14:49
It was a great player.
285
877131
1428
Bio je sjajan takmičar.
14:50
But then, for Final Jeopardy,
Watson was asked this question:
Watson was asked this question:
286
878583
3569
Ali su ga onda u finalnom izazovu
upitali sledeće pitanje:
upitali sledeće pitanje:
"Njegov najveći aerodrom je nazvan
po heroju iz II svetskog rata,
po heroju iz II svetskog rata,
14:54
"Its largest airport is named
for a World War II hero,
for a World War II hero,
287
882659
2932
14:57
its second-largest
for a World War II battle."
for a World War II battle."
288
885615
2252
a drugi po veličini
po bici iz II svetskog rata."
po bici iz II svetskog rata."
14:59
(Hums Final Jeopardy music)
289
887891
1378
(Pevuši finalnu temu iz kviza)
15:01
Chicago.
290
889582
1182
Čikago.
15:02
The two humans got it right.
291
890788
1370
Oba ljudska bića su pogodila.
15:04
Watson, on the other hand,
answered "Toronto" --
answered "Toronto" --
292
892697
4348
Votson, s druge strane,
odgovorio je: "Toronto" -
odgovorio je: "Toronto" -
15:09
for a US city category!
293
897069
1818
za kategoriju gradova SAD-a.
15:11
The impressive system also made an error
294
899596
2901
Impresivni sistem je napravio i grešku
15:14
that a human would never make,
a second-grader wouldn't make.
a second-grader wouldn't make.
295
902521
3651
koju ljudsko biće nikad ne bi,
drugaš je nikad ne bi napravio.
drugaš je nikad ne bi napravio.
15:18
Our machine intelligence can fail
296
906823
3109
Naša mašinska inteligencija može da omane
15:21
in ways that don't fit
error patterns of humans,
error patterns of humans,
297
909956
3100
na načine koji se ne uklapaju
sa obrascima grešenja kod ljudi,
sa obrascima grešenja kod ljudi,
15:25
in ways we won't expect
and be prepared for.
and be prepared for.
298
913080
2950
na načine koji su neočekivani
i na koje nismo pripremljeni.
i na koje nismo pripremljeni.
15:28
It'd be lousy not to get a job
one is qualified for,
one is qualified for,
299
916054
3638
Bilo bi loše ne dobiti posao
za koji ste kvalifikovani,
za koji ste kvalifikovani,
15:31
but it would triple suck
if it was because of stack overflow
if it was because of stack overflow
300
919716
3727
ali bi bilo tri puta gore
ako bi to bilo zbog preopterećenja
ako bi to bilo zbog preopterećenja
u nekakvoj sistemskoj podrutini.
15:35
in some subroutine.
301
923467
1432
15:36
(Laughter)
302
924923
1579
(Smeh)
15:38
In May of 2010,
303
926526
2786
U maju 2010,
15:41
a flash crash on Wall Street
fueled by a feedback loop
fueled by a feedback loop
304
929336
4044
munjevit krah na Vol Stritu
je pokrenut povratnom petljom
je pokrenut povratnom petljom
15:45
in Wall Street's "sell" algorithm
305
933404
3028
u Vol Stritovom algoritmu "prodaja",
15:48
wiped a trillion dollars
of value in 36 minutes.
of value in 36 minutes.
306
936456
4184
izbrisao je vrednost
od trilion dolara za 36 minuta.
od trilion dolara za 36 minuta.
Ne želim da razmišljam
o tome šta znači "greška"
o tome šta znači "greška"
15:53
I don't even want to think
what "error" means
what "error" means
307
941722
2187
15:55
in the context of lethal
autonomous weapons.
autonomous weapons.
308
943933
3589
u kontekstu smrtonosnog
autonomnog oružja.
autonomnog oružja.
16:01
So yes, humans have always made biases.
309
949894
3790
Dakle, da, ljudi su oduvek
bili pristrasni.
bili pristrasni.
16:05
Decision makers and gatekeepers,
310
953708
2176
Donosioci odluka i čuvari informacija,
16:07
in courts, in news, in war ...
311
955908
3493
na sudovima, vestima, u ratu...
16:11
they make mistakes;
but that's exactly my point.
but that's exactly my point.
312
959425
3038
oni greše; ali u tome je poenta.
16:14
We cannot escape
these difficult questions.
these difficult questions.
313
962487
3521
Ne možemo da izbegnemo
ova teška pitanja.
ova teška pitanja.
16:18
We cannot outsource
our responsibilities to machines.
our responsibilities to machines.
314
966596
3516
Ne možemo da delegiramo
naša zaduženja mašinama.
naša zaduženja mašinama.
16:22
(Applause)
315
970676
4208
(Aplauz)
16:29
Artificial intelligence does not give us
a "Get out of ethics free" card.
a "Get out of ethics free" card.
316
977089
4447
Veštačka inteligencija nam ne pruža
besplatnu kartu za "beg od etike".
besplatnu kartu za "beg od etike".
16:34
Data scientist Fred Benenson
calls this math-washing.
calls this math-washing.
317
982742
3381
Naučnik za podatke Fred Benson
to naziva matematičkim ispiranjem.
to naziva matematičkim ispiranjem.
16:38
We need the opposite.
318
986147
1389
Potrebno nam je suprotno.
16:39
We need to cultivate algorithm suspicion,
scrutiny and investigation.
scrutiny and investigation.
319
987560
5388
Moramo da negujemo sumnju u algoritme,
nadzor i istraživanje.
nadzor i istraživanje.
16:45
We need to make sure we have
algorithmic accountability,
algorithmic accountability,
320
993380
3198
Moramo da se postaramo
da imamo algoritamsku odgovrnost,
da imamo algoritamsku odgovrnost,
16:48
auditing and meaningful transparency.
321
996602
2445
proveru i smislenu transparentnost.
16:51
We need to accept
that bringing math and computation
that bringing math and computation
322
999380
3234
Moramo da prihvatimo
da uvođenje matematike i kompjutera
da uvođenje matematike i kompjutera
16:54
to messy, value-laden human affairs
323
1002638
2970
u neuredne ljudske odnose
vođene vrednostima
vođene vrednostima
16:57
does not bring objectivity;
324
1005632
2384
ne donosi objektivnost;
17:00
rather, the complexity of human affairs
invades the algorithms.
invades the algorithms.
325
1008040
3633
već pre složenost ljudskih odnosa
osvaja algoritme.
osvaja algoritme.
17:04
Yes, we can and we should use computation
326
1012148
3487
Da, možemo i treba da koristimo kompjutere
17:07
to help us make better decisions.
327
1015659
2014
kako bi donosili bolje odluke.
17:09
But we have to own up
to our moral responsibility to judgment,
to our moral responsibility to judgment,
328
1017697
5332
Ali moramo da ovladamo
našom moralnom odgovornošću i rasuđivanjem
našom moralnom odgovornošću i rasuđivanjem
17:15
and use algorithms within that framework,
329
1023053
2818
i da koristimo algoritme
unutar tog okvira,
unutar tog okvira,
17:17
not as a means to abdicate
and outsource our responsibilities
and outsource our responsibilities
330
1025895
4935
ne kao sredstva da se odreknemo
i da delegiramo naše odgovornosti
i da delegiramo naše odgovornosti
17:22
to one another as human to human.
331
1030854
2454
nekom drugom, kao čovek čoveku.
17:25
Machine intelligence is here.
332
1033807
2609
Mašinska inteligencija je tu.
17:28
That means we must hold on ever tighter
333
1036440
3421
To znači da se kao nikad pre
moramo čvrsto držati
moramo čvrsto držati
17:31
to human values and human ethics.
334
1039885
2147
ljudskih vrednosti i ljudske etike.
17:34
Thank you.
335
1042056
1154
Hvala vam.
17:35
(Applause)
336
1043234
5020
(Aplauz)
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