TEDGlobal>NYC
Zeynep Tufekci: We're building a dystopia just to make people click on ads
齊娜普圖菲西: 為了讓人們點擊廣告因而造成了反烏托邦
Filmed:
Readability: 4.8
2,866,905 views
科技社會學家齊娜普圖菲西認為：因我們一次又一次的點擊，而造成了由人工智慧驅動的反烏托邦（是一種不得人心、令人恐懼的假想社群或社會）。在這場讓人開眼界的演說中，她說明臉書、Google 和亞馬遜等公司用演算法來誘使我們點擊廣告和駐足他們的網站，同時也控制和安排我們看得到、看不到哪些政治內容或社會資訊。我們面對的真正威脅並不是機器或人工智慧，而是當權者可能會用人工智慧來控制我們。她提出了我們應該如何因應。
Zeynep Tufekci - Techno-sociologist
Techno-sociologist Zeynep Tufekci asks big questions about our societies and our lives, as both algorithms and digital connectivity spread. Full bio
Techno-sociologist Zeynep Tufekci asks big questions about our societies and our lives, as both algorithms and digital connectivity spread. Full bio
Double-click the English transcript below to play the video.
00:12
So when people voice fears
of artificial intelligence,
of artificial intelligence,
0
760
3536
當人們表達出對人工智慧的恐懼，
00:16
very often, they invoke images
of humanoid robots run amok.
of humanoid robots run amok.
1
4320
3976
他們腦中的景象通常是
形象似人的機器人失控殺人。
形象似人的機器人失控殺人。
00:20
You know? Terminator?
2
8320
1240
知道嗎？魔鬼終結者？
00:22
You know, that might be
something to consider,
something to consider,
3
10400
2336
雖然考量那種情況的確沒錯，
00:24
but that's a distant threat.
4
12760
1856
但那是遙遠以後的威脅。
00:26
Or, we fret about digital surveillance
5
14640
3456
或者，我們擔心被數位監視，
00:30
with metaphors from the past.
6
18120
1776
有著來自過去的隱喻。
00:31
"1984," George Orwell's "1984,"
7
19920
2656
喬治歐威爾的《1984》
00:34
it's hitting the bestseller lists again.
8
22600
2280
再度登上了暢銷書的排行榜。
00:37
It's a great book,
9
25960
1416
雖然它是本很棒的書，
00:39
but it's not the correct dystopia
for the 21st century.
for the 21st century.
10
27400
3880
但它並未正確地反映出
21 世紀的反烏托邦。
21 世紀的反烏托邦。
00:44
What we need to fear most
11
32080
1416
我們最需要恐懼的
00:45
is not what artificial intelligence
will do to us on its own,
will do to us on its own,
12
33520
4776
並不是人工智慧本身會對我們怎樣，
00:50
but how the people in power
will use artificial intelligence
will use artificial intelligence
13
38320
4736
而是掌權者會如何運用人工智慧
00:55
to control us and to manipulate us
14
43080
2816
來控制和操縱我們，
00:57
in novel, sometimes hidden,
15
45920
3136
用新穎的、有時隱蔽的、
01:01
subtle and unexpected ways.
16
49080
3016
精細的、出乎意料的方式。
01:04
Much of the technology
17
52120
1856
那些會在不遠的將來
01:06
that threatens our freedom
and our dignity in the near-term future
and our dignity in the near-term future
18
54000
4336
威脅我們自由和尊嚴的科技，
01:10
is being developed by companies
19
58360
1856
多半出自下面這類公司，
01:12
in the business of capturing
and selling our data and our attention
and selling our data and our attention
20
60240
4936
他們攫取我們的注意力和資料，
01:17
to advertisers and others:
21
65200
2256
販售給廣告商和其他對象：
01:19
Facebook, Google, Amazon,
22
67480
3416
臉書、Google、亞馬遜、
01:22
Alibaba, Tencent.
23
70920
1880
阿里巴巴、騰訊。
01:26
Now, artificial intelligence has started
bolstering their business as well.
bolstering their business as well.
24
74040
5496
人工智慧開始鞏固這些公司的事業。
01:31
And it may seem
like artificial intelligence
like artificial intelligence
25
79560
2096
看似人工智慧將是
01:33
is just the next thing after online ads.
26
81680
2856
線上廣告後的下一個產物。
01:36
It's not.
27
84560
1216
並非如此。
01:37
It's a jump in category.
28
85800
2456
它是個大躍進的類別，
01:40
It's a whole different world,
29
88280
2576
一個完全不同的世界，
01:42
and it has great potential.
30
90880
2616
它具有龐大的潛力，
01:45
It could accelerate our understanding
of many areas of study and research.
of many areas of study and research.
31
93520
6920
能夠加速我們對於
許多研究領域的了解。
許多研究領域的了解。
01:53
But to paraphrase
a famous Hollywood philosopher,
a famous Hollywood philosopher,
32
101120
3496
但，轉述一位知名
好萊塢哲學家的說法：
好萊塢哲學家的說法：
01:56
"With prodigious potential
comes prodigious risk."
comes prodigious risk."
33
104640
3640
「驚人的潛力會帶來驚人的風險。」
02:01
Now let's look at a basic fact
of our digital lives, online ads.
of our digital lives, online ads.
34
109120
3936
先談談一個數位生活的
基本面向：線上廣告。
基本面向：線上廣告。
02:05
Right? We kind of dismiss them.
35
113080
2896
我們算是有點輕視了線上的廣告。
02:08
They seem crude, ineffective.
36
116000
1976
它們看似粗糙、無效。
02:10
We've all had the experience
of being followed on the web
of being followed on the web
37
118000
4256
我們都曾經因為在網路上
搜尋或閱讀過某些內容，
搜尋或閱讀過某些內容，
02:14
by an ad based on something
we searched or read.
we searched or read.
38
122280
2776
而老是被一個廣告給跟隨著。
02:17
You know, you look up a pair of boots
39
125080
1856
上網搜尋一雙靴子，
02:18
and for a week, those boots are following
you around everywhere you go.
you around everywhere you go.
40
126960
3376
之後的一週，你到哪兒
都會看見那雙靴子。
都會看見那雙靴子。
02:22
Even after you succumb and buy them,
they're still following you around.
they're still following you around.
41
130360
3656
即使你屈服，買下了它，
它還是到處跟著你。
它還是到處跟著你。
02:26
We're kind of inured to that kind
of basic, cheap manipulation.
of basic, cheap manipulation.
42
134040
3016
我們算是習慣了
那種基本、廉價的操縱，
那種基本、廉價的操縱，
02:29
We roll our eyes and we think,
"You know what? These things don't work."
"You know what? These things don't work."
43
137080
3400
翻個白眼，心想：
「知道嗎？這些沒有用。」
「知道嗎？這些沒有用。」
02:33
Except, online,
44
141720
2096
只除了在線上，
02:35
the digital technologies are not just ads.
45
143840
3600
數位科技並不只是廣告。
02:40
Now, to understand that,
let's think of a physical world example.
let's think of a physical world example.
46
148240
3120
為瞭解這一點，我們先用
實體世界當作例子。
實體世界當作例子。
02:43
You know how, at the checkout counters
at supermarkets, near the cashier,
at supermarkets, near the cashier,
47
151840
4656
你們有沒有看過，在超市結帳台
靠近收銀機的地方，
靠近收銀機的地方，
02:48
there's candy and gum
at the eye level of kids?
at the eye level of kids?
48
156520
3480
會有放在孩子視線高度的
糖果和口香糖？
糖果和口香糖？
02:52
That's designed to make them
whine at their parents
whine at their parents
49
160800
3496
那是設計來讓孩子哀求
02:56
just as the parents
are about to sort of check out.
are about to sort of check out.
50
164320
3080
正在結帳的父母用的。
03:00
Now, that's a persuasion architecture.
51
168040
2640
那是一種說服架構，
03:03
It's not nice, but it kind of works.
52
171160
3096
不太好，但算是有些效用，
03:06
That's why you see it
in every supermarket.
in every supermarket.
53
174280
2040
因此在每個超級市場都看得到。
03:08
Now, in the physical world,
54
176720
1696
在實體世界中，
03:10
such persuasion architectures
are kind of limited,
are kind of limited,
55
178440
2496
這種說服架構有點受限，
03:12
because you can only put
so many things by the cashier. Right?
so many things by the cashier. Right?
56
180960
4816
因為在收銀台那裡
只擺得下那麼點東西，對吧？
只擺得下那麼點東西，對吧？
03:17
And the candy and gum,
it's the same for everyone,
it's the same for everyone,
57
185800
4296
並且每個人看到的
是同樣的糖果和口香糖，
是同樣的糖果和口香糖，
03:22
even though it mostly works
58
190120
1456
這招只對身旁
03:23
only for people who have
whiny little humans beside them.
whiny little humans beside them.
59
191600
4040
有小孩子喋喋不休吵著的大人有用。
03:29
In the physical world,
we live with those limitations.
we live with those limitations.
60
197160
3920
我們生活的實體世界裡有那些限制。
03:34
In the digital world, though,
61
202280
1936
但在數位世界裡，
03:36
persuasion architectures
can be built at the scale of billions
can be built at the scale of billions
62
204240
4320
說服架構的規模可達數十億的等級，
03:41
and they can target, infer, understand
63
209840
3856
它們會瞄準、臆測、了解，
針對個人來部署，
03:45
and be deployed at individuals
64
213720
2896
各個擊破，
03:48
one by one
65
216640
1216
03:49
by figuring out your weaknesses,
66
217880
2136
弄清楚個別的弱點，
03:52
and they can be sent
to everyone's phone private screen,
to everyone's phone private screen,
67
220040
5616
且能傳送到每個人
私人手機的螢幕上，
私人手機的螢幕上，
03:57
so it's not visible to us.
68
225680
2256
別人是看不見的。
03:59
And that's different.
69
227960
1256
那就很不一樣。
04:01
And that's just one of the basic things
that artificial intelligence can do.
that artificial intelligence can do.
70
229240
3576
那只是人工智慧
能做到的基本功能之一。
能做到的基本功能之一。
04:04
Now, let's take an example.
71
232840
1336
讓我舉個例子。
04:06
Let's say you want to sell
plane tickets to Vegas. Right?
plane tickets to Vegas. Right?
72
234200
2696
比如說，你要賣飛往賭城的機票。
04:08
So in the old world, you could think
of some demographics to target
of some demographics to target
73
236920
3496
在舊式的世界裡，你可以想出
某些特徵的人來當目標，
某些特徵的人來當目標，
04:12
based on experience
and what you can guess.
and what you can guess.
74
240440
2520
根據你的經驗和猜測。
04:15
You might try to advertise to, oh,
75
243560
2816
你也可以試著打廣告，
04:18
men between the ages of 25 and 35,
76
246400
2496
像針對 25~35 歲的男性，
04:20
or people who have
a high limit on their credit card,
a high limit on their credit card,
77
248920
3936
或高信用卡額度的人，
04:24
or retired couples. Right?
78
252880
1376
或退休的夫妻，對吧？
04:26
That's what you would do in the past.
79
254280
1816
那是過去的做法。
04:28
With big data and machine learning,
80
256120
2896
有了大量資料和機器學習，
04:31
that's not how it works anymore.
81
259040
1524
方式就不一樣了。
04:33
So to imagine that,
82
261320
2176
試想，
04:35
think of all the data
that Facebook has on you:
that Facebook has on you:
83
263520
3856
想想臉書掌握什麼關於你的資料：
04:39
every status update you ever typed,
84
267400
2536
所有你輸入的動態更新、
04:41
every Messenger conversation,
85
269960
2016
所有的訊息對話、
04:44
every place you logged in from,
86
272000
1880
所有你登入時的所在地、
04:48
all your photographs
that you uploaded there.
that you uploaded there.
87
276400
3176
所有你上傳的照片。
04:51
If you start typing something
and change your mind and delete it,
and change your mind and delete it,
88
279600
3776
如果你開始輸入些內容，
但隨後改變主意而將之刪除，
但隨後改變主意而將之刪除，
04:55
Facebook keeps those
and analyzes them, too.
and analyzes them, too.
89
283400
3200
臉書會保留那些內容和分析它們。
04:59
Increasingly, it tries
to match you with your offline data.
to match you with your offline data.
90
287160
3936
它越來越會試著將你
和你的離線資料做匹配，
和你的離線資料做匹配，
05:03
It also purchases
a lot of data from data brokers.
a lot of data from data brokers.
91
291120
3176
也會向資料仲介商購買許多資料。
05:06
It could be everything
from your financial records
from your financial records
92
294320
3416
從你的財務記錄
05:09
to a good chunk of your browsing history.
93
297760
2120
到你過去瀏覽過的一大堆記錄。
05:12
Right? In the US,
such data is routinely collected,
such data is routinely collected,
94
300360
5416
在美國，這些資料被常規地收集、
05:17
collated and sold.
95
305800
1960
校對和售出。
05:20
In Europe, they have tougher rules.
96
308320
2440
歐洲的規定比較嚴。
05:23
So what happens then is,
97
311680
2200
接下來發生的狀況是
05:26
by churning through all that data,
these machine-learning algorithms --
these machine-learning algorithms --
98
314920
4016
透過攪拌所有這些資料，
這些機器學習演算法──
這些機器學習演算法──
05:30
that's why they're called
learning algorithms --
learning algorithms --
99
318960
2896
這就是為什麼它們
被稱為學習演算法──
被稱為學習演算法──
05:33
they learn to understand
the characteristics of people
the characteristics of people
100
321880
4096
它們學會了解過去購買機票
05:38
who purchased tickets to Vegas before.
101
326000
2520
飛往賭城的人有何特徵。
05:41
When they learn this from existing data,
102
329760
3536
當它們從既有的資料中
學到這些之後，
學到這些之後，
05:45
they also learn
how to apply this to new people.
how to apply this to new people.
103
333320
3816
也學習如何將所學
套用到新的人身上。
套用到新的人身上。
05:49
So if they're presented with a new person,
104
337160
3056
如果交給它們一個新的人，
05:52
they can classify whether that person
is likely to buy a ticket to Vegas or not.
is likely to buy a ticket to Vegas or not.
105
340240
4640
它們能辨識那人可能
或不太可能買機票。
或不太可能買機票。
05:57
Fine. You're thinking,
an offer to buy tickets to Vegas.
an offer to buy tickets to Vegas.
106
345720
5456
好。你心想，不就是提供
購買飛往賭城機票的訊息罷了，
購買飛往賭城機票的訊息罷了，
06:03
I can ignore that.
107
351200
1456
可以忽略它。
06:04
But the problem isn't that.
108
352680
2216
但問題不在那裡。
06:06
The problem is,
109
354920
1576
問題是，
06:08
we no longer really understand
how these complex algorithms work.
how these complex algorithms work.
110
356520
4136
我們已經不能真正了解
這些複雜的演算法如何運作。
這些複雜的演算法如何運作。
06:12
We don't understand
how they're doing this categorization.
how they're doing this categorization.
111
360680
3456
我們不了解它們如何分類。
06:16
It's giant matrices,
thousands of rows and columns,
thousands of rows and columns,
112
364160
4416
它是個巨大的矩陣，
有數以千計的直行和橫列，
有數以千計的直行和橫列，
06:20
maybe millions of rows and columns,
113
368600
1960
也許有上百萬的直行和橫列，
06:23
and not the programmers
114
371320
2640
程式設計者也無法了解，
任何人看到它都無法了解，
06:26
and not anybody who looks at it,
115
374760
1680
06:29
even if you have all the data,
116
377440
1496
即使握有所有的資料，
06:30
understands anymore
how exactly it's operating
how exactly it's operating
117
378960
4616
對於它到底如何運作的了解程度，
絕對不會高於你對我現在
腦中想什麼的了解程度，
腦中想什麼的了解程度，
06:35
any more than you'd know
what I was thinking right now
what I was thinking right now
118
383600
3776
06:39
if you were shown
a cross section of my brain.
a cross section of my brain.
119
387400
3960
如果你單憑看我大腦的切面圖。
06:44
It's like we're not programming anymore,
120
392360
2576
感覺好像我們不是在寫程式了，
06:46
we're growing intelligence
that we don't truly understand.
that we don't truly understand.
121
394960
4400
而是在栽培一種我們不是
真正了解的智慧。
真正了解的智慧。
06:52
And these things only work
if there's an enormous amount of data,
if there's an enormous amount of data,
122
400520
3976
只在資料量非常巨大的情況下
這些才行得通，
這些才行得通，
06:56
so they also encourage
deep surveillance on all of us
deep surveillance on all of us
123
404520
5096
所以他們也助長了
對我們所有人的密切監視，
對我們所有人的密切監視，
07:01
so that the machine learning
algorithms can work.
algorithms can work.
124
409640
2336
這樣機器學習才能行得通。
07:04
That's why Facebook wants
to collect all the data it can about you.
to collect all the data it can about you.
125
412000
3176
那就是為什麼臉書要盡可能
收集關於你的資料。
收集關於你的資料。
07:07
The algorithms work better.
126
415200
1576
這樣演算法效果才會比較好。
07:08
So let's push that Vegas example a bit.
127
416800
2696
讓我們再談談賭城的例子。
07:11
What if the system
that we do not understand
that we do not understand
128
419520
3680
如果這個我們不了解的系統
07:16
was picking up that it's easier
to sell Vegas tickets
to sell Vegas tickets
129
424200
5136
發現比較容易把機票銷售給
07:21
to people who are bipolar
and about to enter the manic phase.
and about to enter the manic phase.
130
429360
3760
即將進入躁症階段的躁鬱症患者。
07:25
Such people tend to become
overspenders, compulsive gamblers.
overspenders, compulsive gamblers.
131
433640
4920
這類人傾向於變成
花錢超支的人、強迫性賭徒。
花錢超支的人、強迫性賭徒。
07:31
They could do this, and you'd have no clue
that's what they were picking up on.
that's what they were picking up on.
132
439280
4456
他們能這麼做，而你完全不知道
那是他們選目標的根據。
那是他們選目標的根據。
07:35
I gave this example
to a bunch of computer scientists once
to a bunch of computer scientists once
133
443760
3616
有次，我把這個例子
給了一群電腦科學家，
給了一群電腦科學家，
07:39
and afterwards, one of them came up to me.
134
447400
2056
之後，其中一人來找我。
07:41
He was troubled and he said,
"That's why I couldn't publish it."
"That's why I couldn't publish it."
135
449480
3520
他感到困擾，說：「那就是
為什麼我們無法發表它。」
為什麼我們無法發表它。」
07:45
I was like, "Couldn't publish what?"
136
453600
1715
我說：「不能發表什麼？」
07:47
He had tried to see whether you can indeed
figure out the onset of mania
figure out the onset of mania
137
455800
5856
他曾嘗試能否在出現臨床症狀前
就預知躁鬱症快發作了，
就預知躁鬱症快發作了，
靠的是分析社交媒體的貼文。
07:53
from social media posts
before clinical symptoms,
before clinical symptoms,
138
461680
3216
07:56
and it had worked,
139
464920
1776
他辦到了，
07:58
and it had worked very well,
140
466720
2056
結果非常成功，
08:00
and he had no idea how it worked
or what it was picking up on.
or what it was picking up on.
141
468800
4880
而他完全不知道是怎麼成功的，
也不知道預測的根據是什麼。
也不知道預測的根據是什麼。
08:06
Now, the problem isn't solved
if he doesn't publish it,
if he doesn't publish it,
142
474840
4416
如果他不發表結果，
問題就沒有解決，
問題就沒有解決，
08:11
because there are already companies
143
479280
1896
因為已經有公司
08:13
that are developing
this kind of technology,
this kind of technology,
144
481200
2536
在發展這種技術，
08:15
and a lot of the stuff
is just off the shelf.
is just off the shelf.
145
483760
2800
很多東西都已經是現成的了。
08:19
This is not very difficult anymore.
146
487240
2576
這已經不是很困難的事了。
08:21
Do you ever go on YouTube
meaning to watch one video
meaning to watch one video
147
489840
3456
你可曾經上 YouTube
原本只是要看一支影片，
原本只是要看一支影片，
08:25
and an hour later you've watched 27?
148
493320
2360
一個小時之後你卻已看了 27 支？
08:28
You know how YouTube
has this column on the right
has this column on the right
149
496760
2496
你可知道 YouTube 在網頁的右欄
08:31
that says, "Up next"
150
499280
2216
擺著「即將播放」的影片，
08:33
and it autoplays something?
151
501520
1816
而且會自動接著播放那些影片？
08:35
It's an algorithm
152
503360
1216
那是種演算法，
08:36
picking what it thinks
that you might be interested in
that you might be interested in
153
504600
3616
選出它認為你可能會感興趣，
08:40
and maybe not find on your own.
154
508240
1536
但不見得會自己去找到的影片。
08:41
It's not a human editor.
155
509800
1256
並不是人類編輯者，
08:43
It's what algorithms do.
156
511080
1416
而是演算法做的。
08:44
It picks up on what you have watched
and what people like you have watched,
and what people like you have watched,
157
512520
4736
它去了解你看過什麼影片，
像你這類的人看過什麼影片，
像你這類的人看過什麼影片，
08:49
and infers that that must be
what you're interested in,
what you're interested in,
158
517280
4216
然後推論出那就是你會感興趣、
想看更多的影片，
08:53
what you want more of,
159
521520
1255
然後呈現更多給你看。
08:54
and just shows you more.
160
522799
1336
08:56
It sounds like a benign
and useful feature,
and useful feature,
161
524159
2201
聽起來是個良性又有用的特色，
08:59
except when it isn't.
162
527280
1200
除了它不是這樣的時候。
09:01
So in 2016, I attended rallies
of then-candidate Donald Trump
of then-candidate Donald Trump
163
529640
6960
在 2016 年，我去了一場
擁護當時還是候選人川普的集會，
擁護當時還是候選人川普的集會，
09:09
to study as a scholar
the movement supporting him.
the movement supporting him.
164
537840
3336
我以學者身份去研究支持他的運動。
09:13
I study social movements,
so I was studying it, too.
so I was studying it, too.
165
541200
3456
我研究社會運動，所以也去研究它。
09:16
And then I wanted to write something
about one of his rallies,
about one of his rallies,
166
544680
3336
接著，我想要針對
他的某次集會寫點什麼，
他的某次集會寫點什麼，
09:20
so I watched it a few times on YouTube.
167
548040
1960
所以就在 YouTube 上
看了幾遍。
看了幾遍。
09:23
YouTube started recommending to me
168
551240
3096
YouTube 開始推薦給我
09:26
and autoplaying to me
white supremacist videos
white supremacist videos
169
554360
4256
並為我自動播放，
白人至上主義的影片，
白人至上主義的影片，
09:30
in increasing order of extremism.
170
558640
2656
一支比一支更極端主義。
09:33
If I watched one,
171
561320
1816
如果我看了一支，
09:35
it served up one even more extreme
172
563160
2976
它就會送上另一支更極端的，
09:38
and autoplayed that one, too.
173
566160
1424
並且自動播放它。
09:40
If you watch Hillary Clinton
or Bernie Sanders content,
or Bernie Sanders content,
174
568320
4536
如果你看的影片內容是
希拉蕊柯林頓或伯尼桑德斯，
希拉蕊柯林頓或伯尼桑德斯，
09:44
YouTube recommends
and autoplays conspiracy left,
and autoplays conspiracy left,
175
572880
4696
YouTube 會推薦並自動播放
陰謀論左派的影片，
陰謀論左派的影片，
09:49
and it goes downhill from there.
176
577600
1760
之後就每況愈下。
09:52
Well, you might be thinking,
this is politics, but it's not.
this is politics, but it's not.
177
580480
3056
你可能會想，這是政治。
但並不是，重點不是政治，
09:55
This isn't about politics.
178
583560
1256
09:56
This is just the algorithm
figuring out human behavior.
figuring out human behavior.
179
584840
3096
這只是猜測人類行為的演算法。
09:59
I once watched a video
about vegetarianism on YouTube
about vegetarianism on YouTube
180
587960
4776
我曾經上 YouTube
看一支關於吃素的影片，
看一支關於吃素的影片，
10:04
and YouTube recommended
and autoplayed a video about being vegan.
and autoplayed a video about being vegan.
181
592760
4936
而 YouTube 推薦並自動播放了
一支關於嚴格素食主義者的影片。
一支關於嚴格素食主義者的影片。
10:09
It's like you're never
hardcore enough for YouTube.
hardcore enough for YouTube.
182
597720
3016
似乎對 YouTube 而言
你的口味永遠都還不夠重。
你的口味永遠都還不夠重。
10:12
(Laughter)
183
600760
1576
（笑聲）
10:14
So what's going on?
184
602360
1560
發生了什麼事？
10:16
Now, YouTube's algorithm is proprietary,
185
604520
3536
YouTube 的演算法是專有的，
10:20
but here's what I think is going on.
186
608080
2360
但我認為發生的事是這樣的：
10:23
The algorithm has figured out
187
611360
2096
演算法發現到，
10:25
that if you can entice people
188
613480
3696
如果誘使人們思索
10:29
into thinking that you can
show them something more hardcore,
show them something more hardcore,
189
617200
3736
你還能提供他們更重口味的東西，
10:32
they're more likely to stay on the site
190
620960
2416
他們就更可能會留在網站上，
10:35
watching video after video
going down that rabbit hole
going down that rabbit hole
191
623400
4416
看一支又一支的影片，
一路掉進兔子洞，
一路掉進兔子洞，
10:39
while Google serves them ads.
192
627840
1680
同時 Google 還給他們看廣告。
10:43
Now, with nobody minding
the ethics of the store,
the ethics of the store,
193
631760
3120
沒人在意商家倫理的情況下，
10:47
these sites can profile people
194
635720
4240
這些網站能夠描繪人的特性，
10:53
who are Jew haters,
195
641680
1920
哪些人痛恨猶太人，
10:56
who think that Jews are parasites
196
644360
2480
認為猶太人是寄生蟲，
11:00
and who have such explicit
anti-Semitic content,
anti-Semitic content,
197
648320
4920
以及哪些人明確地反猶太人，
11:06
and let you target them with ads.
198
654080
2000
讓你針對他們提供廣告。
11:09
They can also mobilize algorithms
199
657200
3536
它們也能動員演算法，
11:12
to find for you look-alike audiences,
200
660760
3136
為你找出相近的觀眾群，
11:15
people who do not have such explicit
anti-Semitic content on their profile
anti-Semitic content on their profile
201
663920
5576
那些側看不怎麼明顯反猶太人，
11:21
but who the algorithm detects
may be susceptible to such messages,
may be susceptible to such messages,
202
669520
6176
但是被演算法偵測出來
很容易受到這類訊息影響的人，
很容易受到這類訊息影響的人，
11:27
and lets you target them with ads, too.
203
675720
1920
讓你針對他們提供廣告。
11:30
Now, this may sound
like an implausible example,
like an implausible example,
204
678680
2736
這可能聽起來像是個
難以置信的例子，
難以置信的例子，
11:33
but this is real.
205
681440
1320
但它是真實的。
11:35
ProPublica investigated this
206
683480
2136
ProPublica 調查了這件事，
11:37
and found that you can indeed
do this on Facebook,
do this on Facebook,
207
685640
3616
且發現你的確可以
在臉書上做到這件事，
在臉書上做到這件事，
11:41
and Facebook helpfully
offered up suggestions
offered up suggestions
208
689280
2416
且臉書很有效地提供建議，
11:43
on how to broaden that audience.
209
691720
1600
告訴你如何拓展觀眾群。
11:46
BuzzFeed tried it for Google,
and very quickly they found,
and very quickly they found,
210
694720
3016
BuzzFeed 用 Google
做了實驗，他們很快發現，
做了實驗，他們很快發現，
11:49
yep, you can do it on Google, too.
211
697760
1736
是的，你也可以在
Google 上這樣做。
Google 上這樣做。
11:51
And it wasn't even expensive.
212
699520
1696
而且甚至不貴。
11:53
The ProPublica reporter
spent about 30 dollars
spent about 30 dollars
213
701240
4416
ProPublica 的記者
花了大約 30 美元
花了大約 30 美元
11:57
to target this category.
214
705680
2240
來針對這個類別。
12:02
So last year, Donald Trump's
social media manager disclosed
social media manager disclosed
215
710600
5296
去年川普的社交媒體經理透露，
12:07
that they were using Facebook dark posts
to demobilize people,
to demobilize people,
216
715920
5336
他們利用臉書的隱藏廣告貼文
來「反動員」選民，
來「反動員」選民，
12:13
not to persuade them,
217
721280
1376
不是勸說或動員他們，
12:14
but to convince them not to vote at all.
218
722680
2800
而是說服他們根本不去投票。
12:18
And to do that,
they targeted specifically,
they targeted specifically,
219
726520
3576
為做到這一點，他們準確設定目標，
12:22
for example, African-American men
in key cities like Philadelphia,
in key cities like Philadelphia,
220
730120
3896
比如像費城這樣
關鍵城市的非裔美國男性，
關鍵城市的非裔美國男性，
12:26
and I'm going to read
exactly what he said.
exactly what he said.
221
734040
2456
讓我把他的話一字不漏讀出來。
12:28
I'm quoting.
222
736520
1216
以下為引述。
12:29
They were using "nonpublic posts
223
737760
3016
他們使用「非公開貼文，
那些貼文的觀看權限
由競選團隊來控制，
由競選團隊來控制，
12:32
whose viewership the campaign controls
224
740800
2176
12:35
so that only the people
we want to see it see it.
we want to see it see it.
225
743000
3776
所以只有我們挑的讀者才看得到。
我們為此建立了模型，
12:38
We modeled this.
226
746800
1216
12:40
It will dramatically affect her ability
to turn these people out."
to turn these people out."
227
748040
4720
會嚴重影響到她（指希拉蕊）
動員那些人去投票的能力。」
動員那些人去投票的能力。」
12:45
What's in those dark posts?
228
753720
2280
那些隱藏廣告貼文中有什麼內容？
12:48
We have no idea.
229
756480
1656
我們不知道。
12:50
Facebook won't tell us.
230
758160
1200
臉書不告訴我們。
12:52
So Facebook also algorithmically
arranges the posts
arranges the posts
231
760480
4376
所以臉書也用演算法的方式
來安排你的朋友
來安排你的朋友
12:56
that your friends put on Facebook,
or the pages you follow.
or the pages you follow.
232
764880
3736
在臉書的貼文或是你追蹤的頁面。
它並不會照時間順序
來呈現所有內容。
來呈現所有內容。
13:00
It doesn't show you
everything chronologically.
everything chronologically.
233
768640
2216
13:02
It puts the order in the way
that the algorithm thinks will entice you
that the algorithm thinks will entice you
234
770880
4816
呈現順序是演算法認為
能引誘你在網站上逗留久一點的順序。
13:07
to stay on the site longer.
235
775720
1840
13:11
Now, so this has a lot of consequences.
236
779040
3376
所以，這麼做有許多後果。
13:14
You may be thinking
somebody is snubbing you on Facebook.
somebody is snubbing you on Facebook.
237
782440
3800
你可能會認為有人在臉書上冷落你。
13:18
The algorithm may never
be showing your post to them.
be showing your post to them.
238
786800
3256
也許是演算法根本沒把
你的貼文呈現給他們看。
你的貼文呈現給他們看。
13:22
The algorithm is prioritizing
some of them and burying the others.
some of them and burying the others.
239
790080
5960
演算法優先呈現其中某些，
而埋藏掉其他的。
而埋藏掉其他的。
13:29
Experiments show
240
797320
1296
實驗顯示，
13:30
that what the algorithm picks to show you
can affect your emotions.
can affect your emotions.
241
798640
4520
演算法選擇呈現給你的內容，
會影響你的情緒。
會影響你的情緒。
13:36
But that's not all.
242
804600
1200
但不止這樣，
13:38
It also affects political behavior.
243
806280
2360
它也會影響政治行為。
13:41
So in 2010, in the midterm elections,
244
809360
4656
在 2010 年的期中選舉時，
13:46
Facebook did an experiment
on 61 million people in the US
on 61 million people in the US
245
814040
5896
臉書做了一個實驗，
對象是美國 6100 萬人，
對象是美國 6100 萬人，
13:51
that was disclosed after the fact.
246
819960
1896
該實驗後來被揭露出來。
13:53
So some people were shown,
"Today is election day,"
"Today is election day,"
247
821880
3416
有些人看到的是「今天是選舉日」，
13:57
the simpler one,
248
825320
1376
簡單的版本，
13:58
and some people were shown
the one with that tiny tweak
the one with that tiny tweak
249
826720
3896
有些人看到的是有
小小調整過的版本，
小小調整過的版本，
14:02
with those little thumbnails
250
830640
2096
用小型照片縮圖來顯示出
14:04
of your friends who clicked on "I voted."
251
832760
2840
你的朋友中按了
「我已投票」的那些人。
「我已投票」的那些人。
14:09
This simple tweak.
252
837000
1400
這是個小小的調整。
14:11
OK? So the pictures were the only change,
253
839520
4296
唯一的差別就是照片，
14:15
and that post shown just once
254
843840
3256
這篇貼文只被顯示出來一次，
14:19
turned out an additional 340,000 voters
255
847120
6056
結果多出了 34 萬的投票者
14:25
in that election,
256
853200
1696
在那次選舉投了票，
14:26
according to this research
257
854920
1696
根據這研究指出，
14:28
as confirmed by the voter rolls.
258
856640
2520
這結果已經由選舉人名冊確認過了。
14:32
A fluke? No.
259
860920
1656
是僥倖嗎？不是。
14:34
Because in 2012,
they repeated the same experiment.
they repeated the same experiment.
260
862600
5360
因為在 2012 年，
他們重覆了同樣的實驗。
他們重覆了同樣的實驗。
14:40
And that time,
261
868840
1736
那一次，
14:42
that civic message shown just once
262
870600
3296
只顯示一次的公民訊息
14:45
turned out an additional 270,000 voters.
263
873920
4440
造成投票者多出了 27 萬人。
14:51
For reference, the 2016
US presidential election
US presidential election
264
879160
5216
供參考用：2016 年
總統大選的結果，
總統大選的結果，
14:56
was decided by about 100,000 votes.
265
884400
3520
大約十萬選票的差距決定了江山。
15:01
Now, Facebook can also
very easily infer what your politics are,
very easily infer what your politics are,
266
889360
4736
臉書也能輕易推論出你的政治傾向，
即使你未曾在臉書上透露過。
15:06
even if you've never
disclosed them on the site.
disclosed them on the site.
267
894120
2256
15:08
Right? These algorithms
can do that quite easily.
can do that quite easily.
268
896400
2520
對吧？那些演算法很輕易就做得到。
15:11
What if a platform with that kind of power
269
899960
3896
一旦具有那種力量的平台決定要使
15:15
decides to turn out supporters
of one candidate over the other?
of one candidate over the other?
270
903880
5040
一位候選人的支持者出來投票，
另一位的則不，會如何呢？
另一位的則不，會如何呢？
15:21
How would we even know about it?
271
909680
2440
我們如何得知發生了這種事？
15:25
Now, we started from someplace
seemingly innocuous --
seemingly innocuous --
272
913560
4136
我們討論的起始點看似無害──
15:29
online adds following us around --
273
917720
2216
線上廣告跟著我們到處出現──
15:31
and we've landed someplace else.
274
919960
1840
但後來卻談到別的現象。
15:35
As a public and as citizens,
275
923480
2456
身為大眾、身為公民，
15:37
we no longer know
if we're seeing the same information
if we're seeing the same information
276
925960
3416
我們不再知道
我們看到的資訊是否相同，
我們看到的資訊是否相同，
15:41
or what anybody else is seeing,
277
929400
1480
或是其他人看到了什麼，
15:43
and without a common basis of information,
278
931680
2576
沒有共同的資訊基礎，
15:46
little by little,
279
934280
1616
漸漸地，
15:47
public debate is becoming impossible,
280
935920
3216
就會變成不可能公開辯論了，
15:51
and we're just at
the beginning stages of this.
the beginning stages of this.
281
939160
2976
我們目前只是在
這個過程的初始階段。
這個過程的初始階段。
15:54
These algorithms can quite easily infer
282
942160
3456
這些演算法很容易推論出
15:57
things like your people's ethnicity,
283
945640
3256
比如你的種族、
16:00
religious and political views,
personality traits,
personality traits,
284
948920
2336
宗教和政治觀點、個人特質、
16:03
intelligence, happiness,
use of addictive substances,
use of addictive substances,
285
951280
3376
智力、快樂程度、
是否使用上癮式物質、
是否使用上癮式物質、
16:06
parental separation, age and genders,
286
954680
3136
父母離異、年齡和性別，
16:09
just from Facebook likes.
287
957840
1960
只從臉書按的讚就能知道。
16:13
These algorithms can identify protesters
288
961440
4056
這些演算法能夠辨識抗議者，
16:17
even if their faces
are partially concealed.
are partially concealed.
289
965520
2760
即使遮蔽他們部份的臉也能辨識。
16:21
These algorithms may be able
to detect people's sexual orientation
to detect people's sexual orientation
290
969720
6616
這些演算法或許能偵測人的性向，
16:28
just from their dating profile pictures.
291
976360
3200
只要有他們的約會側寫照片即可。
16:33
Now, these are probabilistic guesses,
292
981560
2616
這些是用機率算出的猜測，
16:36
so they're not going
to be 100 percent right,
to be 100 percent right,
293
984200
2896
所以不見得 100% 正確，
16:39
but I don't see the powerful resisting
the temptation to use these technologies
the temptation to use these technologies
294
987120
4896
但我並沒有看到因為這些技術有
假陽性結果（實際沒有被預測為有）
假陽性結果（實際沒有被預測為有）
16:44
just because there are
some false positives,
some false positives,
295
992040
2176
大家就抗拒使用它們，
16:46
which will of course create
a whole other layer of problems.
a whole other layer of problems.
296
994240
3256
因而這些假陽性結果
又造成全然另一層的問題。
又造成全然另一層的問題。
16:49
Imagine what a state can do
297
997520
2936
想像一下國家會怎麼用
16:52
with the immense amount of data
it has on its citizens.
it has on its citizens.
298
1000480
3560
所擁有的大量國民資料。
16:56
China is already using
face detection technology
face detection technology
299
1004680
4776
中國已經在使用面部辨識技術
17:01
to identify and arrest people.
300
1009480
2880
來識別和逮捕人。
17:05
And here's the tragedy:
301
1013280
2136
不幸的是，
17:07
we're building this infrastructure
of surveillance authoritarianism
of surveillance authoritarianism
302
1015440
5536
起初我們建立這個
專制監視的基礎結構，
專制監視的基礎結構，
17:13
merely to get people to click on ads.
303
1021000
2960
僅僅為了要讓人們點閱廣告。
17:17
And this won't be
Orwell's authoritarianism.
Orwell's authoritarianism.
304
1025240
2576
這不會是歐威爾的專制主義。
17:19
This isn't "1984."
305
1027839
1897
這不是《1984》。
17:21
Now, if authoritarianism
is using overt fear to terrorize us,
is using overt fear to terrorize us,
306
1029760
4576
如果專制主義
公然利用恐懼來恐嚇我們，
公然利用恐懼來恐嚇我們，
17:26
we'll all be scared, but we'll know it,
307
1034359
2897
我們會害怕，但我們心知肚明，
17:29
we'll hate it and we'll resist it.
308
1037280
2200
我們會厭惡它，也會抗拒它。
17:32
But if the people in power
are using these algorithms
are using these algorithms
309
1040880
4416
但如果掌權者用這些演算法
17:37
to quietly watch us,
310
1045319
3377
悄悄地監看我們、
17:40
to judge us and to nudge us,
311
1048720
2080
評斷我們、輕輕推使我們，
17:43
to predict and identify
the troublemakers and the rebels,
the troublemakers and the rebels,
312
1051720
4176
用這些演算法來預測和辨識出
問題製造者和叛亂份子，
問題製造者和叛亂份子，
17:47
to deploy persuasion
architectures at scale
architectures at scale
313
1055920
3896
部署大規模的說服結構，
17:51
and to manipulate individuals one by one
314
1059840
4136
並個別操弄每一個人，
17:56
using their personal, individual
weaknesses and vulnerabilities,
weaknesses and vulnerabilities,
315
1064000
5440
利用他們個人、個別的缺點和弱點，
18:02
and if they're doing it at scale
316
1070720
2200
如果規模夠大，
18:06
through our private screens
317
1074080
1736
透過我們私人的螢幕，
18:07
so that we don't even know
318
1075840
1656
那麼我們甚至不會知道
18:09
what our fellow citizens
and neighbors are seeing,
and neighbors are seeing,
319
1077520
2760
其他公民及鄰居看到了什麼內容，
18:13
that authoritarianism
will envelop us like a spider's web
will envelop us like a spider's web
320
1081560
4816
那種專制主義會像蜘蛛網
一樣把我們緊緊地包裹起來，
一樣把我們緊緊地包裹起來，
而我們甚至不會知道
自己被包在裡面。
自己被包在裡面。
18:18
and we may not even know we're in it.
321
1086400
2480
18:22
So Facebook's market capitalization
322
1090440
2936
所以，臉書的市場資本化
18:25
is approaching half a trillion dollars.
323
1093400
3296
已經接近五千億美元。
18:28
It's because it works great
as a persuasion architecture.
as a persuasion architecture.
324
1096720
3120
因為它是個很成功的說服架構。
18:33
But the structure of that architecture
325
1101760
2816
但用的架構一樣，
18:36
is the same whether you're selling shoes
326
1104600
3216
不論你銷售的是鞋子
18:39
or whether you're selling politics.
327
1107840
2496
或是政治。
18:42
The algorithms do not know the difference.
328
1110360
3120
演算法不知道差別。
18:46
The same algorithms set loose upon us
329
1114240
3296
那個被鬆綁了的、
為使我們更容易
被廣告左右的演算法，
被廣告左右的演算法，
18:49
to make us more pliable for ads
330
1117560
3176
18:52
are also organizing our political,
personal and social information flows,
personal and social information flows,
331
1120760
6736
同時也正組織著我們的
政治、個人和社會的資訊流，
政治、個人和社會的資訊流，
18:59
and that's what's got to change.
332
1127520
1840
這點必須要被改變才行。
19:02
Now, don't get me wrong,
333
1130240
2296
別誤會我，
我們使用數位平台，是因為
它們能提供我們極大的價值。
它們能提供我們極大的價值。
19:04
we use digital platforms
because they provide us with great value.
because they provide us with great value.
334
1132560
3680
19:09
I use Facebook to keep in touch
with friends and family around the world.
with friends and family around the world.
335
1137120
3560
我用臉書來和世界各地的
朋友家人保持聯絡。
朋友家人保持聯絡。
19:14
I've written about how crucial
social media is for social movements.
social media is for social movements.
336
1142000
5776
我寫過關於社交媒體對於
社會運動有多重要的文章。
社會運動有多重要的文章。
19:19
I have studied how
these technologies can be used
these technologies can be used
337
1147800
3016
我研究過這些技術能如何
被用來規避世界各地的審查制度。
19:22
to circumvent censorship around the world.
338
1150840
2480
19:27
But it's not that the people who run,
you know, Facebook or Google
you know, Facebook or Google
339
1155280
6416
但，不是臉書
或 Google 的營運者
或 Google 的營運者
19:33
are maliciously and deliberately trying
340
1161720
2696
在惡意、刻意地嘗試
19:36
to make the country
or the world more polarized
or the world more polarized
341
1164440
4456
讓國家或世界變得更兩極化、
19:40
and encourage extremism.
342
1168920
1680
或鼓勵極端主義。
19:43
I read the many
well-intentioned statements
well-intentioned statements
343
1171440
3976
我讀過許多出發點很好的聲明，
19:47
that these people put out.
344
1175440
3320
都是這些人發出來的。
19:51
But it's not the intent or the statements
people in technology make that matter,
people in technology make that matter,
345
1179600
6056
但重點並不是科技人的意圖或聲明，
19:57
it's the structures
and business models they're building.
and business models they're building.
346
1185680
3560
而他們建造的結構與商業模型
20:02
And that's the core of the problem.
347
1190360
2096
才是問題的核心。
20:04
Either Facebook is a giant con
of half a trillion dollars
of half a trillion dollars
348
1192480
4720
要不就臉書是個大騙子，
詐騙了半兆美元，
詐騙了半兆美元，
20:10
and ads don't work on the site,
349
1198200
1896
該網站上的廣告沒有用，
20:12
it doesn't work
as a persuasion architecture,
as a persuasion architecture,
350
1200120
2696
它不以說服架構的形式運作；
20:14
or its power of influence
is of great concern.
is of great concern.
351
1202840
4120
要不就它的影響力很讓人擔心。
20:20
It's either one or the other.
352
1208560
1776
只會是兩者其一。
20:22
It's similar for Google, too.
353
1210360
1600
Google 也類似。
20:24
So what can we do?
354
1212880
2456
所以，我們能做什麼？
20:27
This needs to change.
355
1215360
1936
這必須要改變。
20:29
Now, I can't offer a simple recipe,
356
1217320
2576
我無法提供簡單的解決之道，
20:31
because we need to restructure
357
1219920
2256
因為我們得要重建
20:34
the whole way our
digital technology operates.
digital technology operates.
358
1222200
3016
整個數位技術的運作方式；
20:37
Everything from the way
technology is developed
technology is developed
359
1225240
4096
每一樣──從發展技術的方式
20:41
to the way the incentives,
economic and otherwise,
economic and otherwise,
360
1229360
3856
到獎勵的方式，不論是實質
或其他形式的獎勵──
或其他形式的獎勵──
20:45
are built into the system.
361
1233240
2280
都要被建置到系統中。
20:48
We have to face and try to deal with
362
1236480
3456
我們得要面對並試圖處理
20:51
the lack of transparency
created by the proprietary algorithms,
created by the proprietary algorithms,
363
1239960
4656
專有演算法所造成的透明度缺乏，
20:56
the structural challenge
of machine learning's opacity,
of machine learning's opacity,
364
1244640
3816
難懂的機器學習的結構性挑戰，
21:00
all this indiscriminate data
that's being collected about us.
that's being collected about us.
365
1248480
3400
所有被不分皂白地收集走、
與我們相關的資料。
與我們相關的資料。
21:05
We have a big task in front of us.
366
1253000
2520
我們面對巨大的任務。
21:08
We have to mobilize our technology,
367
1256160
2680
我們得要動員我們的科技、
21:11
our creativity
368
1259760
1576
我們的創意、
21:13
and yes, our politics
369
1261360
1880
以及我們的政治。
21:16
so that we can build
artificial intelligence
artificial intelligence
370
1264240
2656
以讓我們建立的人工智慧
21:18
that supports us in our human goals
371
1266920
3120
能夠支持我們人類的目標，
21:22
but that is also constrained
by our human values.
by our human values.
372
1270800
3920
那些同時也被人類價值
所限制住的目標。
所限制住的目標。
21:27
And I understand this won't be easy.
373
1275600
2160
我知道這不容易。
21:30
We might not even easily agree
on what those terms mean.
on what those terms mean.
374
1278360
3600
我們甚至無法輕易取得
那些用語意義的共識。
那些用語意義的共識。
21:34
But if we take seriously
375
1282920
2400
但如果我們認真看待
21:38
how these systems that we
depend on for so much operate,
depend on for so much operate,
376
1286240
5976
我們如此依賴的這些系統如何運作，
21:44
I don't see how we can postpone
this conversation anymore.
this conversation anymore.
377
1292240
4120
我看不出我們怎能再延遲對話。
21:49
These structures
378
1297200
2536
這些結構
21:51
are organizing how we function
379
1299760
4096
正在組織我們運作的方式，
21:55
and they're controlling
380
1303880
2296
並且控制了
21:58
what we can and we cannot do.
381
1306200
2616
我們能做什麼、不能做什麼。
22:00
And many of these ad-financed platforms,
382
1308840
2456
許多這類由廣告贊助的平台，
22:03
they boast that they're free.
383
1311320
1576
它們誇說它們是免費的。
22:04
In this context, it means
that we are the product that's being sold.
that we are the product that's being sold.
384
1312920
4560
在這個情境下，意思就是說
「我們」就是被銷售的產品。
「我們」就是被銷售的產品。
22:10
We need a digital economy
385
1318840
2736
我們需要一個數位經濟結構，
22:13
where our data and our attention
386
1321600
3496
在這個結構中，我們的
資料和注意力是非賣品，
資料和注意力是非賣品，
22:17
is not for sale to the highest-bidding
authoritarian or demagogue.
authoritarian or demagogue.
387
1325120
5080
不能售與出價最高的
專制主義者或煽動者。
專制主義者或煽動者。
22:23
(Applause)
388
1331160
3800
（掌聲）
22:30
So to go back to
that Hollywood paraphrase,
that Hollywood paraphrase,
389
1338480
3256
回到前面說的好萊塢改述，
22:33
we do want the prodigious potential
390
1341760
3736
我們的確希望人工智慧與數位科技的
22:37
of artificial intelligence
and digital technology to blossom,
and digital technology to blossom,
391
1345520
3200
巨大潛能能夠綻放，
22:41
but for that, we must face
this prodigious menace,
this prodigious menace,
392
1349400
4936
但為此，我們必須要
面對這個巨大的威脅，
面對這個巨大的威脅，
22:46
open-eyed and now.
393
1354360
1936
睜開眼睛，現在就做。
22:48
Thank you.
394
1356320
1216
謝謝。
22:49
(Applause)
395
1357560
4640
（掌聲）
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