TEDxMidAtlantic 2013
Jennifer Golbeck: Your social media "likes" expose more than you think
Jennifer Golbeck: Curly fry gåden: Hvorfor likes på sociale medier siger mere end du måske tror.
Filmed:
Readability: 4.5
2,366,837 views
Kan du lide Curly fries? har du liket den på Facebook? Se denne snak for at finde ud af de overraskende ting Facebook (og andre) kan gætte omkring dig fra dine tilfældige likes og delinger. Computerforsker Jennifer Golbeck forklarer hvordan dette opstod, hvordan nogle anvendelser af teknologien ikke er så søde -- og hvorfor hun mener vi burde returnere kontrollen af information til dens retmæssige ejere.
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.
Hvis du kan huske det første årti af internettet
00:12
If you remember that first decade of the web,
0
738
1997
00:14
it was really a static place.
1
2735
2255
det var virkelig et stillestående sted.
00:16
You could go online, you could look at pages,
2
4990
2245
Du kunne gå online, du kunne se på sider
og de var sat op af enten organisationer
00:19
and they were put up either by organizations
3
7235
2513
00:21
who had teams to do it
4
9748
1521
som havde teams til at gøre det
00:23
or by individuals who were really tech-savvy
5
11269
2229
eller af individer der var super teknologihabile
00:25
for the time.
6
13498
1737
på den tid.
00:27
And with the rise of social media
7
15235
1575
Med fremkomsten af de sociale medier
00:28
and social networks in the early 2000s,
8
16810
2399
og sociale netværk i begyndelsen af 2000'erne
00:31
the web was completely changed
9
19209
2149
ændrede internettet sig fuldstændigt
00:33
to a place where now the vast majority of content
10
21358
3608
til et sted hvor nu størstedelen af indholdet
vi interagere med er lagt ud af almindelige brugere
00:36
we interact with is put up by average users,
11
24966
3312
enten som YouTubevideoer eller blogs
00:40
either in YouTube videos or blog posts
12
28278
2697
eller som produktanmeldelse eller opdateringer på sociale medier.
00:42
or product reviews or social media postings.
13
30975
3315
Det blev også et meget mere interaktivt sted,
00:46
And it's also become a much more interactive place,
14
34290
2347
00:48
where people are interacting with others,
15
36637
2637
hvor mennesker interagere med hinanden,
00:51
they're commenting, they're sharing,
16
39274
1696
de kommenterer, de deler
00:52
they're not just reading.
17
40970
1614
de læser ikke bare.
00:54
So Facebook is not the only place you can do this,
18
42584
1866
Så Facebook er ikke det eneste sted du kan gøre dette,
00:56
but it's the biggest,
19
44450
1098
men det er det største,
og det tjener til at illustrere tallene.
00:57
and it serves to illustrate the numbers.
20
45548
1784
00:59
Facebook has 1.2 billion users per month.
21
47332
3477
Facebook har 1.2 billioner brugere per måned.
01:02
So half the Earth's Internet population
22
50809
1930
Det er så halvdelen af Jordens internetbrugere
01:04
is using Facebook.
23
52739
1653
der benytter Facebook.
01:06
They are a site, along with others,
24
54392
1932
Det er en side, som sammen med andre,
01:08
that has allowed people to create an online persona
25
56324
3219
har givet personer mulighed for at opbygge en online personlighed
01:11
with very little technical skill,
26
59543
1782
med meget lidt teknologisk færdighed
01:13
and people responded by putting huge amounts
27
61325
2476
og mennesker reagerede ved at putte kæmpe mængder
01:15
of personal data online.
28
63801
1983
personlig data online.
01:17
So the result is that we have behavioral,
29
65784
2543
Så resultat er at vi har adfærdsmæssige,
01:20
preference, demographic data
30
68327
1986
præferencemæssige og demografisk data
01:22
for hundreds of millions of people,
31
70313
2101
for hundrede af millioner mennesker,
01:24
which is unprecedented in history.
32
72414
2026
Hvilket er hidtil uset i historien.
01:26
And as a computer scientist,
what this means is that
what this means is that
33
74440
2560
Og som computerforsker, hvad det betyder er,
01:29
I've been able to build models
34
77000
1664
at jeg er blevet i stand til at bygge modeller
01:30
that can predict all sorts of hidden attributes
35
78664
2322
der kan forudse alle mulige skjulte egenskaber
01:32
for all of you that you don't even know
36
80986
2284
for alle jer, som jeg ikke engang kender,
01:35
you're sharing information about.
37
83270
2202
som i deler information omkring.
01:37
As scientists, we use that to help
38
85472
2382
Som forsker, bruger vi det til at hjælpe
01:39
the way people interact online,
39
87854
2114
den måde mennesker interagere online,
01:41
but there's less altruistic applications,
40
89968
2499
men der er mere egoistiske anvendelser,
01:44
and there's a problem in that users don't really
41
92467
2381
og der er et problem i at brugere ikke rigtigt
01:46
understand these techniques and how they work,
42
94848
2470
forstår disse teknikker og hvordan de virker,
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
og selv hvis de gjorde, har de ikke meget kontrol over det.
01:52
So what I want to talk to you about today
44
100446
1490
Så hvad jeg gerne vil tale om i dag
01:53
is some of these things that we're able to do,
45
101936
2702
er nogle af de ting som vi er istand til at gøre
01:56
and then give us some ideas
of how we might go forward
of how we might go forward
46
104638
2763
og så give jer nogle ideer om
hvordan vi nok vil tage fat
hvordan vi nok vil tage fat
01:59
to move some control back into the hands of users.
47
107401
2769
for at flytte noget af kontrollen tilbage til brugerne.
02:02
So this is Target, the company.
48
110170
1586
Det her er firmaet Target.
02:03
I didn't just put that logo
49
111756
1324
jeg har ikke blot sat dette logo
02:05
on this poor, pregnant woman's belly.
50
113080
2170
på denne fattige, gravide dames mave.
02:07
You may have seen this anecdote that was printed
51
115250
1840
Du har måske set denne anekdote der var trykket
02:09
in Forbes magazine where Target
52
117090
2061
i Forbes magasin hvor Target
02:11
sent a flyer to this 15-year-old girl
53
119151
2361
sendte en flyer til denne 15-årige pige
02:13
with advertisements and coupons
54
121512
1710
med reklamer og kuponer
02:15
for baby bottles and diapers and cribs
55
123222
2554
til babyflasker og bleer og vugger
02:17
two weeks before she told her parents
56
125776
1684
to uger før hun fortalte hendes forældre
02:19
that she was pregnant.
57
127460
1864
at hun var gravid.
02:21
Yeah, the dad was really upset.
58
129324
2704
Ja, faren var virkelig oprevet.
02:24
He said, "How did Target figure out
59
132028
1716
Han sagde, "hvordan fandt Target ud af
02:25
that this high school girl was pregnant
60
133744
1824
at denne gymnasiepige var gravid
02:27
before she told her parents?"
61
135568
1960
før hun fortalte det til hendes forældre?"
02:29
It turns out that they have the purchase history
62
137528
2621
Det viser sig, at de har købshistorikken
02:32
for hundreds of thousands of customers
63
140149
2301
for hundredetusinde kunder
02:34
and they compute what they
call a pregnancy score,
call a pregnancy score,
64
142450
2730
og de beregner hvad de kalder en graviditetsscore,
02:37
which is not just whether or
not a woman's pregnant,
not a woman's pregnant,
65
145180
2332
der ikke bare er hvorvidt en kvinde er gravid eller ej,
02:39
but what her due date is.
66
147512
1730
men hvornår hendes termin er.
02:41
And they compute that
67
149242
1304
De beregner dette
02:42
not by looking at the obvious things,
68
150546
1768
ikke ved at kigge på de åbenlyse ting,
02:44
like, she's buying a crib or baby clothes,
69
152314
2512
som, hun køber en vugge eller baby tøj,
02:46
but things like, she bought more vitamins
70
154826
2943
men ting som, hun købte flere vitaminer
02:49
than she normally had,
71
157769
1717
end hun normalt gjorde,
02:51
or she bought a handbag
72
159486
1464
eller hun købte en håndtaske
02:52
that's big enough to hold diapers.
73
160950
1711
der er stor nok til at indeholde bleer.
02:54
And by themselves, those purchases don't seem
74
162661
1910
For i sig selv, virker disse køb ikke til
02:56
like they might reveal a lot,
75
164571
2469
at de ville afsløre en masse,
02:59
but it's a pattern of behavior that,
76
167040
1978
men der er et adfærdsmønster som,
03:01
when you take it in the context
of thousands of other people,
of thousands of other people,
77
169018
3117
når du tager det i konteksten af flere tusinde af mennesker,
03:04
starts to actually reveal some insights.
78
172135
2757
rent faktisk begynder at give noget indsigt.
03:06
So that's the kind of thing that we do
79
174892
1793
Så det er den slags ting vi gør
03:08
when we're predicting stuff
about you on social media.
about you on social media.
80
176685
2567
når vi forudsiger ting omkring dig på de sociale medier.
03:11
We're looking for little
patterns of behavior that,
patterns of behavior that,
81
179252
2796
Vi leder efter små adfærdsmønster der,
03:14
when you detect them among millions of people,
82
182048
2682
når du opdager dem imellem millioner af mennesker,
03:16
lets us find out all kinds of things.
83
184730
2706
lader os finde ud af alle mulige ting
03:19
So in my lab and with colleagues,
84
187436
1747
Så i mit laboratorie med kollegaer,
03:21
we've developed mechanisms where we can
85
189183
1777
har vi udviklet mekanismer hvor vi
03:22
quite accurately predict things
86
190960
1560
rimelig præcist kan forudsige ting
03:24
like your political preference,
87
192520
1725
som din politiske præference,
03:26
your personality score, gender, sexual orientation,
88
194245
3752
din personlighedscore, køn, seksuelle orientering,
03:29
religion, age, intelligence,
89
197997
2873
religion, alder, intelligens,
03:32
along with things like
90
200870
1394
sammen med ting som
03:34
how much you trust the people you know
91
202264
1937
hvor meget du stoler på de mennesker du kender
03:36
and how strong those relationships are.
92
204201
1804
og hvor stærke disse forhold er.
03:38
We can do all of this really well.
93
206005
1785
Vi kan gøre alt dette ret godt.
03:39
And again, it doesn't come from what you might
94
207790
2197
Og igen, det kommer ikke fra hvad du måske
tror er den åbenlyse information.
03:41
think of as obvious information.
95
209987
2102
03:44
So my favorite example is from this study
96
212089
2281
Så mit favorit eksempel fra en undersøgelse
03:46
that was published this year
97
214370
1240
der blev udgivet dette år
03:47
in the Proceedings of the National Academies.
98
215610
1795
i "Proceedings of the National Academies"
03:49
If you Google this, you'll find it.
99
217405
1285
Hvis du Googler dette, finder du det.
03:50
It's four pages, easy to read.
100
218690
1872
Det er fire sider, let at læse.
03:52
And they looked at just people's Facebook likes,
101
220562
3003
og de kiggede bare på folks Facebook likes,
03:55
so just the things you like on Facebook,
102
223565
1920
så bare de ting du kan lide på Facebook,
03:57
and used that to predict all these attributes,
103
225485
2138
og brugte dette til at forudsige disse attributter.
03:59
along with some other ones.
104
227623
1645
sammen med nogle yderligere.
04:01
And in their paper they listed the five likes
105
229268
2961
I deres artikel oplistede de fem likes
04:04
that were most indicative of high intelligence.
106
232229
2787
der mest indikerede en høj intelligens.
04:07
And among those was liking a page
107
235016
2324
Iblandt dem var at like en side
04:09
for curly fries. (Laughter)
108
237340
1905
for curly fries. (latter)
04:11
Curly fries are delicious,
109
239245
2093
Curly fries er lækre,
04:13
but liking them does not necessarily mean
110
241338
2530
men at kunne lide dem betyder ikke nødvendigvis,
04:15
that you're smarter than the average person.
111
243868
2080
at du er klogere end den gennemsnitlige person.
04:17
So how is it that one of the strongest indicators
112
245948
3207
Så hvordan kan det være at en af de stærkeste indikatorer
04:21
of your intelligence
113
249155
1570
på din intelligens
04:22
is liking this page
114
250725
1447
er at like denne side
04:24
when the content is totally irrelevant
115
252172
2252
når indholdet er totalt irrelevant
04:26
to the attribute that's being predicted?
116
254424
2527
for de attributter der bliver forudsagt?
04:28
And it turns out that we have to look at
117
256951
1584
Og det viser sig, at vi er nødt til at se på
04:30
a whole bunch of underlying theories
118
258535
1618
en hel bunke underliggende teorier
04:32
to see why we're able to do this.
119
260153
2569
for at se hvorfor vi er i stand til det.
04:34
One of them is a sociological
theory called homophily,
theory called homophily,
120
262722
2913
En af dem er en sociologisk teori kaldet homofili
som dybest set siger,
at folk er venner med folk som ligner dem.
at folk er venner med folk som ligner dem.
04:37
which basically says people are
friends with people like them.
friends with people like them.
121
265635
3092
04:40
So if you're smart, you tend to
be friends with smart people,
be friends with smart people,
122
268727
2014
Så hvis du er klog, har du en tendens til,
at være venner med kloge mennesker
at være venner med kloge mennesker
04:42
and if you're young, you tend
to be friends with young people,
to be friends with young people,
123
270741
2630
og hvis du er ung, tendere du til
at være venner med unge mennesker,
at være venner med unge mennesker,
04:45
and this is well established
124
273371
1627
og dette er veletableret
04:46
for hundreds of years.
125
274998
1745
i hundrede af år.
04:48
We also know a lot
126
276743
1232
Vi ved også en masse
04:49
about how information spreads through networks.
127
277975
2550
omkring hvordan information spreder sig igennem netværk.
04:52
It turns out things like viral videos
128
280525
1754
Det viser sig at ting som virale videoer
04:54
or Facebook likes or other information
129
282279
2406
eller Facebook likes eller anden information
04:56
spreads in exactly the same way
130
284685
1888
spreder sig på nøjagtig samme måde
04:58
that diseases spread through social networks.
131
286573
2454
som sygdom spredes gennem sociale netværk.
05:01
So this is something we've studied for a long time.
132
289027
1791
Dette er noget vi har studeret i lang tid.
05:02
We have good models of it.
133
290818
1576
Vi har gode modeller af det.
05:04
And so you can put those things together
134
292394
2157
og så kan du sætte de ting sammen
05:06
and start seeing why things like this happen.
135
294551
3088
og begynde at se hvorfor ting som dette sker
05:09
So if I were to give you a hypothesis,
136
297639
1814
Hvis jeg skulle give jer en hypotese,
05:11
it would be that a smart guy started this page,
137
299453
3227
ville det være at en klog fyr startede denne side,
05:14
or maybe one of the first people who liked it
138
302680
1939
eller måske en af de første personer der likede den
05:16
would have scored high on that test.
139
304619
1736
ville have scoret højere på den test.
05:18
And they liked it, and their friends saw it,
140
306355
2288
De likede den, og deres venner så det,
05:20
and by homophily, we know that
he probably had smart friends,
he probably had smart friends,
141
308643
3122
og via homofili, ved vi, at han højst
sandsynlig havde kloge venner,
sandsynlig havde kloge venner,
05:23
and so it spread to them,
and some of them liked it,
and some of them liked it,
142
311765
3056
så det spreder sig til dem, og nogle af dem likede det,
05:26
and they had smart friends,
143
314821
1189
og de havde kloge venner,
05:28
and so it spread to them,
144
316010
807
og så spredte det sig til dem,
05:28
and so it propagated through the network
145
316817
1973
og sådan forplantede det sig igennem netværket
05:30
to a host of smart people,
146
318790
2569
til et væld af kloge personer,
05:33
so that by the end, the action
147
321359
2056
så i sidste ende, handlingen
05:35
of liking the curly fries page
148
323415
2544
af at like curly fries siden
05:37
is indicative of high intelligence,
149
325959
1615
er indikator af høj intelligens
05:39
not because of the content,
150
327574
1803
ikke pga. indholdet
05:41
but because the actual action of liking
151
329377
2522
men fordi den faktiske handling af at like
05:43
reflects back the common attributes
152
331899
1900
afspejler den fælles attribut
05:45
of other people who have done it.
153
333799
2468
af de mennesker som har gjort det.
05:48
So this is pretty complicated stuff, right?
154
336267
2897
Det er en rimeligt kompliceret ting, ikke?
05:51
It's a hard thing to sit down and explain
155
339164
2199
det er svært at sætte sig ned og forklare
05:53
to an average user, and even if you do,
156
341363
2848
til en gennemsnitlig bruger, og hvis du gør det,
05:56
what can the average user do about it?
157
344211
2188
hvad kan brugeren gøre ved det?
05:58
How do you know that
you've liked something
you've liked something
158
346399
2048
Hvordan ved du at du likede noget
06:00
that indicates a trait for you
159
348447
1492
der indikere et træk ved dig
06:01
that's totally irrelevant to the
content of what you've liked?
content of what you've liked?
160
349939
3545
der er totalt irrelevant for det indhold af det du likede?
06:05
There's a lot of power that users don't have
161
353484
2546
Der er en stor magt som brugere ikke
06:08
to control how this data is used.
162
356030
2230
har til at kontrollere hvordan denne data er brugt.
06:10
And I see that as a real
problem going forward.
problem going forward.
163
358260
3112
Og jeg ser det som et reelt problem fremadrettet
06:13
So I think there's a couple paths
164
361372
1977
Så jeg tror der er et par veje
06:15
that we want to look at
165
363349
1001
som vi skal se på
06:16
if we want to give users some control
166
364350
1910
hvis vi vil give brugerne noget kontrol
06:18
over how this data is used,
167
366260
1740
omkring hvordan denne data er brugt,
06:20
because it's not always going to be used
168
368000
1940
fordi det ikke altid bliver brugt
06:21
for their benefit.
169
369940
1381
til deres fordel.
06:23
An example I often give is that,
170
371321
1422
Et eksempel jeg tit giver er, at
06:24
if I ever get bored being a professor,
171
372743
1646
hvis jeg nogensinde blev træt af at være professor
06:26
I'm going to go start a company
172
374389
1653
vil jeg starte et firma
06:28
that predicts all of these attributes
173
376042
1454
der forudsiger alle disse attributter
06:29
and things like how well you work in teams
174
377496
1602
og ting som hvor godt du arbejder i teams
06:31
and if you're a drug user, if you're an alcoholic.
175
379098
2671
og om du er stofmisbruger, om du er alkoholiker
06:33
We know how to predict all that.
176
381769
1440
Vi ved hvordan vi kan forudsige alt dette.
06:35
And I'm going to sell reports
177
383209
1761
og jeg vil sælge nogle rapporter
06:36
to H.R. companies and big businesses
178
384970
2100
til HR firmaer og store virksomheder
06:39
that want to hire you.
179
387070
2273
der vil hyre dig.
06:41
We totally can do that now.
180
389343
1177
Det kan vi altså gøre nu.
06:42
I could start that business tomorrow,
181
390520
1788
Jeg kunne starte dette firma imorgen,
06:44
and you would have
absolutely no control
absolutely no control
182
392308
2052
og du ville have absolut ingen kontrol
06:46
over me using your data like that.
183
394360
2138
over at jeg bruger dine data som det.
06:48
That seems to me to be a problem.
184
396498
2292
Det virker for mig som et problem.
06:50
So one of the paths we can go down
185
398790
1910
Så en af de veje vi kan gå
06:52
is the policy and law path.
186
400700
2032
er politik og lovvejen
06:54
And in some respects, I think
that that would be most effective,
that that would be most effective,
187
402732
3046
Og i nogle henseende, tror jeg
at dette ville være mest effektivt,
at dette ville være mest effektivt,
06:57
but the problem is we'd
actually have to do it.
actually have to do it.
188
405778
2756
men problemet er at vi
faktisk ville skulle gøre det.
faktisk ville skulle gøre det.
07:00
Observing our political process in action
189
408534
2780
Ved observering af vores aktive politiske processer
07:03
makes me think it's highly unlikely
190
411314
2379
får det mig til at tro at det er meget usandsynligt
07:05
that we're going to get a bunch of representatives
191
413693
1597
at vi kan få en bunke repræsentanter
07:07
to sit down, learn about this,
192
415290
1986
til at sidde sig ned, lære om dette,
07:09
and then enact sweeping changes
193
417276
2106
og så vedtage gennemgribende ændringer
07:11
to intellectual property law in the U.S.
194
419382
2157
for den intellektuelle ejendomsrets lov i USA
07:13
so users control their data.
195
421539
2461
så brugerne kan kontrollere deres data.
07:16
We could go the policy route,
196
424000
1304
Vi kunne gå politik vejen,
07:17
where social media companies say,
197
425304
1479
hvor sociale medie virksomheder siger,
07:18
you know what? You own your data.
198
426783
1402
ved du hvad? Du ejer din egne data.
07:20
You have total control over how it's used.
199
428185
2489
Du har total kontrol over hvordan det bliver brugt.
07:22
The problem is that the revenue models
200
430674
1848
Problemet er, at indtægtsmodellerne
07:24
for most social media companies
201
432522
1724
for de fleste sociale medier
07:26
rely on sharing or exploiting
users' data in some way.
users' data in some way.
202
434246
4031
afhænger af deling eller udnyttelse af brugers data.
07:30
It's sometimes said of Facebook that the users
203
438277
1833
Det er nogle gange sagt af Facebook at brugerne
07:32
aren't the customer, they're the product.
204
440110
2528
ikke er forbrugerne, de er produktet.
07:34
And so how do you get a company
205
442638
2714
Så hvordan får du et firma
07:37
to cede control of their main asset
206
445352
2558
til at give kontrollen af deres primære aktiv
07:39
back to the users?
207
447910
1249
tilbage til brugerne?
07:41
It's possible, but I don't think it's something
208
449159
1701
Det er muligt, men jeg tror ikke det er noget
07:42
that we're going to see change quickly.
209
450860
2320
som vi vil se ske hurtigt.
07:45
So I think the other path
210
453180
1500
Så jeg tror den anden vej
07:46
that we can go down that's
going to be more effective
going to be more effective
211
454680
2288
som vi kan gå ned af som vil være mere effektiv
07:48
is one of more science.
212
456968
1508
er en af mere videnskab.
07:50
It's doing science that allowed us to develop
213
458476
2510
Det er at lave forsøg der tillod os at udvikle
07:52
all these mechanisms for computing
214
460986
1750
de mekanismer til beregning
07:54
this personal data in the first place.
215
462736
2052
af personlig data i første omgang.
07:56
And it's actually very similar research
216
464788
2106
Og det er faktisk meget lignende forskning
07:58
that we'd have to do
217
466894
1438
som vi ville skulle lave
08:00
if we want to develop mechanisms
218
468332
2386
hvis vi ville udvikle mekanismer
08:02
that can say to a user,
219
470718
1421
der kan fortælle en bruger,
08:04
"Here's the risk of that action you just took."
220
472139
2229
"her er risikoen ved det du lige har foretaget dig"
08:06
By liking that Facebook page,
221
474368
2080
Ved at like den Facebook sige,
08:08
or by sharing this piece of personal information,
222
476448
2535
eller ved at dele dette stykke personlige information,
08:10
you've now improved my ability
223
478983
1502
har du forbedret min evne
08:12
to predict whether or not you're using drugs
224
480485
2086
til at forudsige hvorvidt du bruger stoffer
08:14
or whether or not you get
along well in the workplace.
along well in the workplace.
225
482571
2862
eller om du ikke kommer godt ud af det på arbejdspladsen.
08:17
And that, I think, can affect whether or not
226
485433
1848
og det, tror jeg, kan påvirker hvorvidt
08:19
people want to share something,
227
487281
1510
folk vil dele noget,
08:20
keep it private, or just keep it offline altogether.
228
488791
3239
holde det privat, eller bare holde det offline.
08:24
We can also look at things like
229
492030
1563
Vi kan også se på ting som
08:25
allowing people to encrypt data that they upload,
230
493593
2728
give folk lov til at kryptere data de uploader,
08:28
so it's kind of invisible and worthless
231
496321
1855
så det bliver usynligt og værdiløst
08:30
to sites like Facebook
232
498176
1431
for sites som Facebook
08:31
or third party services that access it,
233
499607
2629
eller tredjeparts servicer der har adgang til det
08:34
but that select users who the person who posted it
234
502236
3247
men som udvalgte brugere som
personen der postede det
personen der postede det
08:37
want to see it have access to see it.
235
505483
2670
vil have til at se det - kan få adgang til det.
08:40
This is all super exciting research
236
508153
2166
Dette er superspændende forskning
08:42
from an intellectual perspective,
237
510319
1620
fra et intellektuelt perspektiv,
08:43
and so scientists are going to be willing to do it.
238
511939
1859
så forskere vil være villige til at gøre det.
08:45
So that gives us an advantage over the law side.
239
513798
3610
Så det giver os en fordel overfor den lovmæssige side.
08:49
One of the problems that people bring up
240
517408
1725
Et af de problemer folk bringer på banen
08:51
when I talk about this is, they say,
241
519133
1595
når jeg taler om dette er, siger de
08:52
you know, if people start
keeping all this data private,
keeping all this data private,
242
520728
2646
du ved, hvis folk begynder at holde alle disse data private,
08:55
all those methods that you've been developing
243
523374
2113
alle disse metoder som du har udviklet
08:57
to predict their traits are going to fail.
244
525487
2653
til at forudsige deres evner vil mislykkes.
09:00
And I say, absolutely, and for me, that's success,
245
528140
3520
og jeg siger, absolut, og for mig, er det succes.
09:03
because as a scientist,
246
531660
1786
Fordi som forsker,
09:05
my goal is not to infer information about users,
247
533446
3688
er mit mål ikke at udlede information om brugere,
09:09
it's to improve the way people interact online.
248
537134
2767
det er at forbedre den måde folk interagere online.
09:11
And sometimes that involves
inferring things about them,
inferring things about them,
249
539901
3218
og nogle gange involvere det
udledning af ting om dem
udledning af ting om dem
09:15
but if users don't want me to use that data,
250
543119
3022
men hvis brugere ikke vil have mig til at bruge de data
09:18
I think they should have the right to do that.
251
546141
2038
tror jeg de skulle have retten til at gøre det.
09:20
I want users to be informed and consenting
252
548179
2651
Jeg vil have informerede og samtykkende
09:22
users of the tools that we develop.
253
550830
2112
brugerne af de værktøjer vi udvikler.
09:24
And so I think encouraging this kind of science
254
552942
2952
Så jeg tror at opfordre til denne form for videnskab
09:27
and supporting researchers
255
555894
1346
og støtte forskere
09:29
who want to cede some of that control back to users
256
557240
3023
som vil tage noget af denne kontrol tilbage til brugerne
09:32
and away from the social media companies
257
560263
2311
og væk fra de sociale medie virksomheder
09:34
means that going forward, as these tools evolve
258
562574
2671
betyder at gå fremad, som disse værktøjer udvikles
09:37
and advance,
259
565245
1476
og bliver bedre,
09:38
means that we're going to have an educated
260
566721
1414
vil vi være nødt til at have en veluddannet
09:40
and empowered user base,
261
568135
1694
og selvstændig brugerbase
09:41
and I think all of us can agree
262
569829
1100
og jeg tror at vi alle kan blive enige om
09:42
that that's a pretty ideal way to go forward.
263
570929
2564
at det er en ret ideel måde at gå fremad på.
09:45
Thank you.
264
573493
2184
Tak.
(Bifald)
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