TEDxMidAtlantic 2013
Jennifer Golbeck: Your social media "likes" expose more than you think
Dženifer Golbek (Jennifer Golbeck): Zagonetka uvijenog pomfrita - zašto "lajkovi" na društvenim mrežama otkrivaju više nego što mislite
Filmed:
Readability: 4.5
2,366,837 views
Volite li uvijeni pomfrit? Da li ste ga lajkovali na Fejsbuku? Pogledajte ovaj govor kako biste otkrili iznenađujuće stvari koje Fejsbuk (i drugi) mogu da pogode o vama na osnovu nasumičnih "lajkova" i stvari koje ste podelili sa drugima. Informatičarka Dženifer Golbek objašnjava kako je do ovoga došlo, kako neke primene ove tehnologije i nisu tako simpatične, i iznosi zašto ona misli da kontrolu nad informacijama treba da vratimo njihovim pravim vlasnicima.
Jennifer Golbeck - Computer scientist
As the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions. Full bio
As the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions. Full bio
Double-click the English transcript below to play the video.
00:12
If you remember that first decade of the web,
0
738
1997
Ako se sećate prve decenije mreže,
00:14
it was really a static place.
1
2735
2255
bilo je to prilično statično mesto.
00:16
You could go online, you could look at pages,
2
4990
2245
Mogli ste da se umrežite,
da gledate stranice,
da gledate stranice,
00:19
and they were put up either by organizations
3
7235
2513
njih su postavljale ili organizacije
00:21
who had teams to do it
4
9748
1521
koje su imale timove koji su to radili
00:23
or by individuals who were really tech-savvy
5
11269
2229
ili pojedinci koji su bili
prilično tehnološki napredni
prilično tehnološki napredni
00:25
for the time.
6
13498
1737
za to vreme.
00:27
And with the rise of social media
7
15235
1575
Sa usponom društvenih medija
00:28
and social networks in the early 2000s,
8
16810
2399
i društvenih mreža početkom 2000-ih,
00:31
the web was completely changed
9
19209
2149
internet mreža se potpuno transformisala
00:33
to a place where now the vast majority of content
10
21358
3608
u mesto gde većinu sadržaja
00:36
we interact with is put up by average users,
11
24966
3312
sa kojim komuniciramo
postavlja prosečan korisnik,
postavlja prosečan korisnik,
00:40
either in YouTube videos or blog posts
12
28278
2697
bilo preko video snimaka
na Jutjubu ili blog unosa
na Jutjubu ili blog unosa
00:42
or product reviews or social media postings.
13
30975
3315
ili recenzija proizvoda
ili poruka na društvenim mrežama.
ili poruka na društvenim mrežama.
00:46
And it's also become a much more interactive place,
14
34290
2347
Mreža je postala mesto
sa mnogo više komunikacije
sa mnogo više komunikacije
00:48
where people are interacting with others,
15
36637
2637
gde se ljudi povezuju jedni sa drugima,
komentarišu i dele,
00:51
they're commenting, they're sharing,
16
39274
1696
a ne samo čitaju.
00:52
they're not just reading.
17
40970
1614
Fejsbuk nije jedino mesto
gde je ovo moguće,
gde je ovo moguće,
00:54
So Facebook is not the only place you can do this,
18
42584
1866
00:56
but it's the biggest,
19
44450
1098
ali svakako jeste najveće
00:57
and it serves to illustrate the numbers.
20
45548
1784
i služi kao ilustracija stvarnih cifara.
00:59
Facebook has 1.2 billion users per month.
21
47332
3477
Fejsbuk broji 1,2 milijarde
korisnika mesečno.
korisnika mesečno.
01:02
So half the Earth's Internet population
22
50809
1930
Dakle, gotovo polovina
internet korisnika na planeti
internet korisnika na planeti
01:04
is using Facebook.
23
52739
1653
koristi Fejsbuk.
01:06
They are a site, along with others,
24
54392
1932
To je internet stranica,
koja je, kao i mnoge druge,
koja je, kao i mnoge druge,
01:08
that has allowed people to create an online persona
25
56324
3219
omogućila ljudima da stvore
virtualne ličnosti
virtualne ličnosti
01:11
with very little technical skill,
26
59543
1782
sa jako malo tehničkih sposobnosti,
01:13
and people responded by putting huge amounts
27
61325
2476
i ljudi su odreagovali
postavljajući ogromne količine
postavljajući ogromne količine
01:15
of personal data online.
28
63801
1983
ličnih podataka na mrežu.
Rezultat toga je da postoje podaci
01:17
So the result is that we have behavioral,
29
65784
2543
o ponašanju, izborima, demografiji
01:20
preference, demographic data
30
68327
1986
stotina miliona ljudi,
01:22
for hundreds of millions of people,
31
70313
2101
što je neviđeno u istoriji.
01:24
which is unprecedented in history.
32
72414
2026
Za mene, kao informatičara,
01:26
And as a computer scientist,
what this means is that
what this means is that
33
74440
2560
to znači da sam u mogućnosti
da stvorim modele
da stvorim modele
01:29
I've been able to build models
34
77000
1664
01:30
that can predict all sorts of hidden attributes
35
78664
2322
koji mogu prognozirati
svakakve vrste skrivenih osobina
svakakve vrste skrivenih osobina
01:32
for all of you that you don't even know
36
80986
2284
svih vas, za koje ni ne znate
da ih zapravo delite.
01:35
you're sharing information about.
37
83270
2202
Kao naučnici, mi to koristimo
da pomognemo ljudima
da pomognemo ljudima
01:37
As scientists, we use that to help
38
85472
2382
01:39
the way people interact online,
39
87854
2114
da komuniciraju na mreži,
01:41
but there's less altruistic applications,
40
89968
2499
ali postoje i manje altruistične koristi,
01:44
and there's a problem in that users don't really
41
92467
2381
i problem je da korisnici zapravo
01:46
understand these techniques and how they work,
42
94848
2470
ne razumeju ove metode
i kako one funkcionišu,
i kako one funkcionišu,
01:49
and even if they did, they don't
have a lot of control over it.
have a lot of control over it.
43
97318
3128
a sve i da razumeju,
ne mogu mnogo da ih kontrolišu.
ne mogu mnogo da ih kontrolišu.
Dakle, danas želim da govorim
01:52
So what I want to talk to you about today
44
100446
1490
01:53
is some of these things that we're able to do,
45
101936
2702
o nekim od stvari koje smo mi
u mogućnosti da uradimo
u mogućnosti da uradimo
01:56
and then give us some ideas
of how we might go forward
of how we might go forward
46
104638
2763
i da dam neke ideje o tome
kako da vratimo kontrolu korisnicima.
01:59
to move some control back into the hands of users.
47
107401
2769
Ovo je kompanija Target.
02:02
So this is Target, the company.
48
110170
1586
02:03
I didn't just put that logo
49
111756
1324
Nisam samo stavila taj logo
02:05
on this poor, pregnant woman's belly.
50
113080
2170
na stomak ove sirote trudnice.
Možda ste pročitali anegdotu
u Forbs magazinu
u Forbs magazinu
02:07
You may have seen this anecdote that was printed
51
115250
1840
02:09
in Forbes magazine where Target
52
117090
2061
gde je Target poslao flajer
02:11
sent a flyer to this 15-year-old girl
53
119151
2361
jednoj petnaestogodišnjakinji
02:13
with advertisements and coupons
54
121512
1710
sa reklamama i kuponima
02:15
for baby bottles and diapers and cribs
55
123222
2554
za pelene, cucle i krevetiće,
dve nedelje pre nego što je ona
rekla svojim roditeljima
rekla svojim roditeljima
02:17
two weeks before she told her parents
56
125776
1684
02:19
that she was pregnant.
57
127460
1864
da je trudna.
02:21
Yeah, the dad was really upset.
58
129324
2704
O da, otac je bio zaista uznemiren.
02:24
He said, "How did Target figure out
59
132028
1716
Rekao je: "Kako je Target znao
02:25
that this high school girl was pregnant
60
133744
1824
da je ova srednjoškolka trudna,
pre njenih roditelja?"
pre njenih roditelja?"
02:27
before she told her parents?"
61
135568
1960
02:29
It turns out that they have the purchase history
62
137528
2621
Oni zapravo imaju istoriju kupovine
02:32
for hundreds of thousands of customers
63
140149
2301
za stotine hiljada korisnika
i izračunavaju ono što nazivaju
trudničkim rezultatom,
trudničkim rezultatom,
02:34
and they compute what they
call a pregnancy score,
call a pregnancy score,
64
142450
2730
02:37
which is not just whether or
not a woman's pregnant,
not a woman's pregnant,
65
145180
2332
što nije samo
da li je neka žena trudna,
da li je neka žena trudna,
02:39
but what her due date is.
66
147512
1730
nego i kada treba da se porodi.
Oni to izračunaju
02:41
And they compute that
67
149242
1304
ne samo gledajući očigledne stvari,
02:42
not by looking at the obvious things,
68
150546
1768
02:44
like, she's buying a crib or baby clothes,
69
152314
2512
kao što je kupovina krevetića,
dečije odeće,
dečije odeće,
nego i to da je kupovala vitamine
više nego inače,
više nego inače,
02:46
but things like, she bought more vitamins
70
154826
2943
02:49
than she normally had,
71
157769
1717
02:51
or she bought a handbag
72
159486
1464
ili da je kupila torbu,
dovoljno veliku za pelene.
dovoljno veliku za pelene.
02:52
that's big enough to hold diapers.
73
160950
1711
02:54
And by themselves, those purchases don't seem
74
162661
1910
Same po sebi ove kupovine
ne izgledaju kao da mnogo otkrivaju,
02:56
like they might reveal a lot,
75
164571
2469
ali predstavljaju obrazac ponašanja koji,
02:59
but it's a pattern of behavior that,
76
167040
1978
kada se stavi u kontekst
hiljada drugih ljudi
hiljada drugih ljudi
03:01
when you take it in the context
of thousands of other people,
of thousands of other people,
77
169018
3117
počinje da otkriva
neke skrivene činjenice.
neke skrivene činjenice.
03:04
starts to actually reveal some insights.
78
172135
2757
03:06
So that's the kind of thing that we do
79
174892
1793
To je ono što mi radimo
03:08
when we're predicting stuff
about you on social media.
about you on social media.
80
176685
2567
kada pokušavamo da predvidimo stvari
o vama na društvenim mrežama.
o vama na društvenim mrežama.
03:11
We're looking for little
patterns of behavior that,
patterns of behavior that,
81
179252
2796
Tražimo male obrasce u ponašanju koji,
kada ih spazite među milionima ljudi,
03:14
when you detect them among millions of people,
82
182048
2682
dozvoljavaju da saznamo svakakve stvari.
03:16
lets us find out all kinds of things.
83
184730
2706
03:19
So in my lab and with colleagues,
84
187436
1747
Moje kolege i ja smo u laboratoriji
03:21
we've developed mechanisms where we can
85
189183
1777
razradili mehanizme gde smo u mogućnosti
03:22
quite accurately predict things
86
190960
1560
da sasvim tačno predvidimo
03:24
like your political preference,
87
192520
1725
vašu političku opredeljenost,
rezultat testa ličnosti,
rod, seksualnu orijentaciju,
rod, seksualnu orijentaciju,
03:26
your personality score, gender, sexual orientation,
88
194245
3752
versku opredeljenost,
godište, nivo inteligencije,
godište, nivo inteligencije,
03:29
religion, age, intelligence,
89
197997
2873
kao i koliko imate poverenja
u ljude koje poznajete.
u ljude koje poznajete.
03:32
along with things like
90
200870
1394
03:34
how much you trust the people you know
91
202264
1937
i koliko su te veze jake.
03:36
and how strong those relationships are.
92
204201
1804
Sve ovo mi radimo veoma dobro.
03:38
We can do all of this really well.
93
206005
1785
Opet, to ne dolazi
iz očiglednih informacija,
iz očiglednih informacija,
03:39
And again, it doesn't come from what you might
94
207790
2197
03:41
think of as obvious information.
95
209987
2102
kao što bi bilo za očekivati.
03:44
So my favorite example is from this study
96
212089
2281
Moj omiljeni primer je iz ove studije
03:46
that was published this year
97
214370
1240
koja je izdata ove godine
03:47
in the Proceedings of the National Academies.
98
215610
1795
u Zborniku Nacionalnih akademija.
03:49
If you Google this, you'll find it.
99
217405
1285
Ako pretražite internet
moći ćete da nađete.
moći ćete da nađete.
03:50
It's four pages, easy to read.
100
218690
1872
Ima četiri strane, lako je za čitanje.
03:52
And they looked at just people's Facebook likes,
101
220562
3003
Oni su pregledali
samo lajkove ljudi na Fejsbuku,
samo lajkove ljudi na Fejsbuku,
03:55
so just the things you like on Facebook,
102
223565
1920
dakle samo stvari
koje ste lajkovali na Fejsbuku,
koje ste lajkovali na Fejsbuku,
03:57
and used that to predict all these attributes,
103
225485
2138
i iskoristili su ih da predvide
sve ove atribute,
sve ove atribute,
03:59
along with some other ones.
104
227623
1645
zajedno sa nekim drugim.
04:01
And in their paper they listed the five likes
105
229268
2961
U svom izveštaju su nabrojali 5 lajkova
04:04
that were most indicative of high intelligence.
106
232229
2787
koji predstavljaju nagoveštaje
visoke inteligencije.
visoke inteligencije.
04:07
And among those was liking a page
107
235016
2324
Jedan od lajkova je
04:09
for curly fries. (Laughter)
108
237340
1905
stranica uvijenih prženih krompirića.
(Smeh)
(Smeh)
04:11
Curly fries are delicious,
109
239245
2093
Uvijeni prženi krompirići su ukusni,
04:13
but liking them does not necessarily mean
110
241338
2530
ali to što vam se dopadaju ne znači nužno
04:15
that you're smarter than the average person.
111
243868
2080
da ste pametniji od prosečne osobe.
04:17
So how is it that one of the strongest indicators
112
245948
3207
Kako je onda jedan od bitnijih indikatora
vaše inteligencije
vaše inteligencije
04:21
of your intelligence
113
249155
1570
lajkovanje ove stranice,
04:22
is liking this page
114
250725
1447
04:24
when the content is totally irrelevant
115
252172
2252
kada je sadržaj potpuno nebitan
04:26
to the attribute that's being predicted?
116
254424
2527
u odnosu na atribut koji se predviđa?
04:28
And it turns out that we have to look at
117
256951
1584
Ispostavlja se da moramo da uzmemo u obzir
04:30
a whole bunch of underlying theories
118
258535
1618
gomilu drugih teorija,
04:32
to see why we're able to do this.
119
260153
2569
da bismo saznali kako dolazimo
do ovog rezultata.
do ovog rezultata.
04:34
One of them is a sociological
theory called homophily,
theory called homophily,
120
262722
2913
Jedna od teorija je sociološka,
zvana homofilija,
zvana homofilija,
04:37
which basically says people are
friends with people like them.
friends with people like them.
121
265635
3092
koja kaže da prijatelji imaju
zajednička interesovanja.
zajednička interesovanja.
04:40
So if you're smart, you tend to
be friends with smart people,
be friends with smart people,
122
268727
2014
Ako ste pametni,
često su to i vaši prijatelji,
često su to i vaši prijatelji,
04:42
and if you're young, you tend
to be friends with young people,
to be friends with young people,
123
270741
2630
ako ste mladi, sprijateljićete se
sa drugim mladim osobama
sa drugim mladim osobama
04:45
and this is well established
124
273371
1627
i ovo je već davno ustanovljeno.
04:46
for hundreds of years.
125
274998
1745
04:48
We also know a lot
126
276743
1232
Mi takođe znamo mnogo tome
04:49
about how information spreads through networks.
127
277975
2550
kako se informacije prenose kroz mreže.
04:52
It turns out things like viral videos
128
280525
1754
Ispostavlja se da se popularni video
04:54
or Facebook likes or other information
129
282279
2406
ili lajkovi na Fejsbuku
i druge informacije,
i druge informacije,
04:56
spreads in exactly the same way
130
284685
1888
prenose identično
kao i bolesti kroz društvene mreže.
kao i bolesti kroz društvene mreže.
04:58
that diseases spread through social networks.
131
286573
2454
05:01
So this is something we've studied for a long time.
132
289027
1791
Ovo izučavamo već duže vreme.
05:02
We have good models of it.
133
290818
1576
Imamo dobre modele za to.
05:04
And so you can put those things together
134
292394
2157
Kada sve to saberete
05:06
and start seeing why things like this happen.
135
294551
3088
uvidećete zašto se tako nešto
uopšte dešava.
uopšte dešava.
Ako bih vam ponudila hipotezu
05:09
So if I were to give you a hypothesis,
136
297639
1814
05:11
it would be that a smart guy started this page,
137
299453
3227
ona bi glasila da je neki bistar momak
napravio ovu stranicu,
napravio ovu stranicu,
05:14
or maybe one of the first people who liked it
138
302680
1939
ili da je jedna od prvih osoba
koja je lajkovala stranicu
koja je lajkovala stranicu
05:16
would have scored high on that test.
139
304619
1736
imala visoke rezultate
na testu inteligencije.
na testu inteligencije.
05:18
And they liked it, and their friends saw it,
140
306355
2288
Njihovi prijatelji su to videli
05:20
and by homophily, we know that
he probably had smart friends,
he probably had smart friends,
141
308643
3122
i na osnovu homofilije, pretpostavljamo
da je imao pametne prijatelje,
da je imao pametne prijatelje,
05:23
and so it spread to them,
and some of them liked it,
and some of them liked it,
142
311765
3056
pa se sve prenelo na njih,
pa su i oni lajkovali,
pa su i oni lajkovali,
05:26
and they had smart friends,
143
314821
1189
a i oni su imali pametne prijatelje
05:28
and so it spread to them,
144
316010
807
05:28
and so it propagated through the network
145
316817
1973
pa se sve takođe prenelo na njih,
05:30
to a host of smart people,
146
318790
2569
pa se sve proširilo kroz mrežu
na veliki broj pametnih ljudi,
05:33
so that by the end, the action
147
321359
2056
i do kraja je postupak
05:35
of liking the curly fries page
148
323415
2544
lajkovanja stranice uvijenih krompirića
05:37
is indicative of high intelligence,
149
325959
1615
postao indikativan
za visoku inteligenciju,
za visoku inteligenciju,
05:39
not because of the content,
150
327574
1803
ne zbog svog sadržaja,
05:41
but because the actual action of liking
151
329377
2522
nego zbog toga što čin lajkovanja
odražava zajedničke osobine
05:43
reflects back the common attributes
152
331899
1900
05:45
of other people who have done it.
153
333799
2468
drugih ljudi koji su učinili isto.
05:48
So this is pretty complicated stuff, right?
154
336267
2897
Ovo je prilično komplikovano, zar ne?
05:51
It's a hard thing to sit down and explain
155
339164
2199
Nije lako objasniti prosečnom korisniku,
05:53
to an average user, and even if you do,
156
341363
2848
a čak iako uspete,
05:56
what can the average user do about it?
157
344211
2188
šta prosečan korisnik
može da uradi povodom toga?
može da uradi povodom toga?
05:58
How do you know that
you've liked something
you've liked something
158
346399
2048
Kako da znate da ste lajkovali nešto
06:00
that indicates a trait for you
159
348447
1492
što odaje neku vašu osobinu,
06:01
that's totally irrelevant to the
content of what you've liked?
content of what you've liked?
160
349939
3545
a koja nema nikakve veze
sa sadržajem koji ste lajkovali?
sa sadržajem koji ste lajkovali?
Korisnici nemaju mnogo moći
06:05
There's a lot of power that users don't have
161
353484
2546
da kontrolišu kako se ovi podaci koriste.
06:08
to control how this data is used.
162
356030
2230
06:10
And I see that as a real
problem going forward.
problem going forward.
163
358260
3112
Ja to vidim kao pravi problem.
06:13
So I think there's a couple paths
164
361372
1977
Mislim da postoje dva puta
06:15
that we want to look at
165
363349
1001
koja želimo da razmatramo
06:16
if we want to give users some control
166
364350
1910
ako želimo da korisnicima damo kontrolu
06:18
over how this data is used,
167
366260
1740
nad korišćenjem podataka,
06:20
because it's not always going to be used
168
368000
1940
jer oni neće uvek biti korišćeni
06:21
for their benefit.
169
369940
1381
u njihovu korist.
06:23
An example I often give is that,
170
371321
1422
Jedan primer koji često dajem je da,
06:24
if I ever get bored being a professor,
171
372743
1646
ako mi ikada dosadi da budem profesor,
06:26
I'm going to go start a company
172
374389
1653
osnovaću kompaniju
06:28
that predicts all of these attributes
173
376042
1454
koja predviđa sve ove osobine,
06:29
and things like how well you work in teams
174
377496
1602
npr. koliko dobro radite u timovima,
06:31
and if you're a drug user, if you're an alcoholic.
175
379098
2671
da li koristite droge,
da li ste alkoholičar.
da li ste alkoholičar.
06:33
We know how to predict all that.
176
381769
1440
Znamo kako sve to da predvidimo.
06:35
And I'm going to sell reports
177
383209
1761
I prodavaću izveštaje
06:36
to H.R. companies and big businesses
178
384970
2100
HR kompanijama i velikim firmama
06:39
that want to hire you.
179
387070
2273
koje žele da vas zaposle.
To apsolutno možemo sada da uradimo.
06:41
We totally can do that now.
180
389343
1177
06:42
I could start that business tomorrow,
181
390520
1788
Mogla bih sutra da otvorim tu kompaniju,
06:44
and you would have
absolutely no control
absolutely no control
182
392308
2052
a vi uopšte ne biste imali kontrolu
06:46
over me using your data like that.
183
394360
2138
nad tim kako ja koristim vaše podatke.
06:48
That seems to me to be a problem.
184
396498
2292
Po mom mišljenju, ovo je problem.
06:50
So one of the paths we can go down
185
398790
1910
Jedan od puteva kojim možemo da pođemo
06:52
is the policy and law path.
186
400700
2032
je put pravila i zakona.
06:54
And in some respects, I think
that that would be most effective,
that that would be most effective,
187
402732
3046
Donekle, mislim da bi to bilo
i najefikasnije,
i najefikasnije,
ali problem je u tome
što bi to trebalo i uradimo.
što bi to trebalo i uradimo.
06:57
but the problem is we'd
actually have to do it.
actually have to do it.
188
405778
2756
Gledajući kako
naš politički proces funkcioniše,
naš politički proces funkcioniše,
07:00
Observing our political process in action
189
408534
2780
07:03
makes me think it's highly unlikely
190
411314
2379
izgleda mi malo verovatno
da će gomila političara
07:05
that we're going to get a bunch of representatives
191
413693
1597
07:07
to sit down, learn about this,
192
415290
1986
da sedne i nauči nešto o ovome,
07:09
and then enact sweeping changes
193
417276
2106
a onda sprovede korenite promene
07:11
to intellectual property law in the U.S.
194
419382
2157
u zakonu o intelektualnoj svojini u SAD-u,
07:13
so users control their data.
195
421539
2461
kako bi korisnici
kontrolisali svoje podatke.
kontrolisali svoje podatke.
07:16
We could go the policy route,
196
424000
1304
Mogli bismo da idemo putem politike,
07:17
where social media companies say,
197
425304
1479
gde kompanije društvenih medija kažu:
07:18
you know what? You own your data.
198
426783
1402
"Vi posedujete svoje podatke.
07:20
You have total control over how it's used.
199
428185
2489
Imate potpunu kontrolu
nad njihovim korišćenjem."
nad njihovim korišćenjem."
07:22
The problem is that the revenue models
200
430674
1848
Problem je u tome što se modeli poslovanja
07:24
for most social media companies
201
432522
1724
većine kompanija društvenih medija
07:26
rely on sharing or exploiting
users' data in some way.
users' data in some way.
202
434246
4031
oslanjaju na deljenje i iskorišćavanje
podataka korisnika na neki način.
podataka korisnika na neki način.
07:30
It's sometimes said of Facebook that the users
203
438277
1833
Za Fejsbuk se nekada kaže
07:32
aren't the customer, they're the product.
204
440110
2528
da korisnici nisu klijenti,
nego su proizvod.
nego su proizvod.
07:34
And so how do you get a company
205
442638
2714
I kako da navedete neku kompaniju
da kontrolu nad svojim glavnim resursom
07:37
to cede control of their main asset
206
445352
2558
07:39
back to the users?
207
447910
1249
vrati korisnicima?
07:41
It's possible, but I don't think it's something
208
449159
1701
Moguće je, ali mislim da nije nešto
07:42
that we're going to see change quickly.
209
450860
2320
što će se brzo promeniti.
07:45
So I think the other path
210
453180
1500
Mislim da je drugi put
07:46
that we can go down that's
going to be more effective
going to be more effective
211
454680
2288
kojim možemo da krenemo,
i koji je efektniji,
i koji je efektniji,
07:48
is one of more science.
212
456968
1508
je onaj sa više nauke.
07:50
It's doing science that allowed us to develop
213
458476
2510
Primena nauke nam je omogućila
da uopšte razvijemo sve ove mehanizme
izračunavanja
izračunavanja
07:52
all these mechanisms for computing
214
460986
1750
07:54
this personal data in the first place.
215
462736
2052
ovih ličnih podataka.
07:56
And it's actually very similar research
216
464788
2106
To istraživanje je veoma slično onom
07:58
that we'd have to do
217
466894
1438
koje bismo morali da sprovedemo
08:00
if we want to develop mechanisms
218
468332
2386
ako bismo želeli da razvijemo mehanizme
08:02
that can say to a user,
219
470718
1421
koji bi korisniku rekli:
08:04
"Here's the risk of that action you just took."
220
472139
2229
"Ovo je rizik akcije
koju ste upravo sproveli".
koju ste upravo sproveli".
08:06
By liking that Facebook page,
221
474368
2080
Lajkovanjem te Fejsbuk stranice
08:08
or by sharing this piece of personal information,
222
476448
2535
ili deljenjem te lične informacije
08:10
you've now improved my ability
223
478983
1502
poboljšali ste moju sposobnost
08:12
to predict whether or not you're using drugs
224
480485
2086
da predvidim da li koristite droge
08:14
or whether or not you get
along well in the workplace.
along well in the workplace.
225
482571
2862
ili da li se dobro slažete sa kolegama
na poslu.
na poslu.
08:17
And that, I think, can affect whether or not
226
485433
1848
I mislim da će to uticati na odluku
ljudi da podele nešto,
ljudi da podele nešto,
08:19
people want to share something,
227
487281
1510
da zadrže u privatnosti
ili uopšte ne postave na internet.
ili uopšte ne postave na internet.
08:20
keep it private, or just keep it offline altogether.
228
488791
3239
08:24
We can also look at things like
229
492030
1563
Možemo posmatrati
08:25
allowing people to encrypt data that they upload,
230
493593
2728
i dozvoljavanje ljudima da šifrom
zaštite podatke koje postavljaju,
zaštite podatke koje postavljaju,
08:28
so it's kind of invisible and worthless
231
496321
1855
da bi bili nevidljivi i beskorisni
sajtovima kao što je Fejsbuk
sajtovima kao što je Fejsbuk
08:30
to sites like Facebook
232
498176
1431
08:31
or third party services that access it,
233
499607
2629
ili servisima trećeg lica
koji im pristupaju,
koji im pristupaju,
08:34
but that select users who the person who posted it
234
502236
3247
ali da mogu da vide
samo odabrani korisnici,
samo odabrani korisnici,
za koje osoba koja postavlja sadržaj,
želi da vide.
želi da vide.
08:37
want to see it have access to see it.
235
505483
2670
08:40
This is all super exciting research
236
508153
2166
Ovo je veoma uzbudljivo istraživanje,
08:42
from an intellectual perspective,
237
510319
1620
sa intelektualnog stanovišta,
08:43
and so scientists are going to be willing to do it.
238
511939
1859
i naučnici će želeti time da se bave.
08:45
So that gives us an advantage over the law side.
239
513798
3610
To nam daje prednost u odnosu na zakon.
Jedan od problema koji ljudi iznose
08:49
One of the problems that people bring up
240
517408
1725
08:51
when I talk about this is, they say,
241
519133
1595
kada govorim o ovome je,
08:52
you know, if people start
keeping all this data private,
keeping all this data private,
242
520728
2646
da ako ljudi počnu
da podatke drže u tajnosti,
da podatke drže u tajnosti,
08:55
all those methods that you've been developing
243
523374
2113
sve metode koje sam ja razvijala
08:57
to predict their traits are going to fail.
244
525487
2653
za predviđanje njihovih osobina
će propasti.
će propasti.
09:00
And I say, absolutely, and for me, that's success,
245
528140
3520
Ja se apsolutno slažem,
za mene je to uspeh,
za mene je to uspeh,
jer kao naučniku,
09:03
because as a scientist,
246
531660
1786
09:05
my goal is not to infer information about users,
247
533446
3688
meni nije cilj da nagađam
o podacima korisnika,
o podacima korisnika,
nego da poboljšam način
na koji komuniciraju na internetu.
na koji komuniciraju na internetu.
09:09
it's to improve the way people interact online.
248
537134
2767
09:11
And sometimes that involves
inferring things about them,
inferring things about them,
249
539901
3218
Ponekad to uključuje zaključivanje,
ali ako korisnici ne žele
da koristim njihove podatke,
da koristim njihove podatke,
09:15
but if users don't want me to use that data,
250
543119
3022
mislim da bi trebalo da imaju pravo na to.
09:18
I think they should have the right to do that.
251
546141
2038
09:20
I want users to be informed and consenting
252
548179
2651
Želim da korisnici budu informisani
i da pristanu
i da pristanu
da koriste alate koje razvijamo.
09:22
users of the tools that we develop.
253
550830
2112
09:24
And so I think encouraging this kind of science
254
552942
2952
Mislim da podsticanje ovakve nauke
09:27
and supporting researchers
255
555894
1346
i podržavanje istraživača
09:29
who want to cede some of that control back to users
256
557240
3023
koji žele da deo te kontrole
vrate korisnicima
vrate korisnicima
09:32
and away from the social media companies
257
560263
2311
i oduzmu od kompanija društvenih medija,
09:34
means that going forward, as these tools evolve
258
562574
2671
znači napredovanje,
kako se ti alati razvijaju
kako se ti alati razvijaju
09:37
and advance,
259
565245
1476
i napreduju,
09:38
means that we're going to have an educated
260
566721
1414
znači da ćemo imati obrazovanu
i osnaženu bazu korisnika,
i osnaženu bazu korisnika,
09:40
and empowered user base,
261
568135
1694
i mislim da se slažemo
09:41
and I think all of us can agree
262
569829
1100
09:42
that that's a pretty ideal way to go forward.
263
570929
2564
da je to idealan način za napredovanje.
09:45
Thank you.
264
573493
2184
Hvala vam.
(Aplauz)
09:47
(Applause)
265
575677
3080
ABOUT THE SPEAKER
Jennifer Golbeck - Computer scientistAs the director of the Human-Computer Interaction Lab at the University of Maryland, Jennifer Golbeck studies how people use social media -- and thinks about ways to improve their interactions.
Why you should listen
Jennifer Golbeck is an associate professor in the College of Information Studies at the University of Maryland, where she also moonlights in the department of computer science. Her work invariably focuses on how to enhance and improve the way that people interact with their own information online. "I approach this from a computer science perspective and my general research hits social networks, trust, web science, artificial intelligence, and human-computer interaction," she writes.
Author of the 2013 book, Analyzing the Social Web, Golbeck likes nothing more than to immerse herself in the inner workings of the Internet tools so many millions of people use daily, to understand the implications of our choices and actions. Recently, she has also been working to bring human-computer interaction ideas to the world of security and privacy systems.
Jennifer Golbeck | Speaker | TED.com