TED2017
Cathy O'Neil: The era of blind faith in big data must end
凯西·奥尼尔: 盲目信仰大数据的时代必须结束
Filmed:
Readability: 4.4
1,391,460 views
算法决定谁会得到贷款，谁会得到工作面试，谁会得到保险等等—— 但它们不会自动使事情变得公平。身为数学家兼数据科学家的凯西·奥尼尔为算法创造了一个术语，它们是秘密的、重要的和有害的：“杀伤性数学武器”。通过这个演讲了解更多关于这些公式背后不为人知的运作方式吧。
Cathy O'Neil - Mathematician, data scientist
Data skeptic Cathy O’Neil uncovers the dark secrets of big data, showing how our "objective" algorithms could in fact reinforce human bias. Full bio
Data skeptic Cathy O’Neil uncovers the dark secrets of big data, showing how our "objective" algorithms could in fact reinforce human bias. Full bio
Double-click the English transcript below to play the video.
00:12
Algorithms are everywhere.
0
975
1596
算法无处不在。
00:16
They sort and separate
the winners from the losers.
the winners from the losers.
1
4111
3125
他们把成功者和失败者区分开来。
00:20
The winners get the job
2
8019
2264
成功者得到工作
或是一个很好的信用卡优惠计划。
00:22
or a good credit card offer.
3
10307
1743
失败者甚至连面试机会都没有，
00:24
The losers don't even get an interview
4
12074
2651
00:27
or they pay more for insurance.
5
15590
1777
或者要为保险付更多的钱。
00:30
We're being scored with secret formulas
that we don't understand
that we don't understand
6
18197
3549
我们被不理解的秘密公式打分，
00:34
that often don't have systems of appeal.
7
22675
3217
却并没有上诉的渠道。
00:39
That begs the question:
8
27240
1296
这引出了一个问题：
如果算法是错误的怎么办？
00:40
What if the algorithms are wrong?
9
28560
2913
00:45
To build an algorithm you need two things:
10
33100
2040
构建一个算法需要两个要素：
需要数据，如过去发生的事情，
00:47
you need data, what happened in the past,
11
35164
1981
和成功的定义，
00:49
and a definition of success,
12
37169
1561
你正在寻找的，通常希望得到的东西。
00:50
the thing you're looking for
and often hoping for.
and often hoping for.
13
38754
2457
你可以通过观察，理解来训练算法。
00:53
You train an algorithm
by looking, figuring out.
by looking, figuring out.
14
41235
5037
这种算法能找出与成功相关的因素。
00:58
The algorithm figures out
what is associated with success.
what is associated with success.
15
46296
3419
什么情况意味着成功？
01:01
What situation leads to success?
16
49739
2463
01:04
Actually, everyone uses algorithms.
17
52881
1762
其实，每个人都使用算法。
01:06
They just don't formalize them
in written code.
in written code.
18
54667
2718
他们只是没有把它们写成书面代码。
举个例子。
01:09
Let me give you an example.
19
57409
1348
我每天都用一种算法来
为我的家人做饭。
为我的家人做饭。
01:10
I use an algorithm every day
to make a meal for my family.
to make a meal for my family.
20
58781
3316
01:14
The data I use
21
62121
1476
我使用的数据
01:16
is the ingredients in my kitchen,
22
64394
1659
就是我厨房里的原料，
我拥有的时间，
01:18
the time I have,
23
66077
1527
我的热情，
01:19
the ambition I have,
24
67628
1233
然后我整理了这些数据。
01:20
and I curate that data.
25
68885
1709
我不把那种小包拉面算作食物。
01:22
I don't count those little packages
of ramen noodles as food.
of ramen noodles as food.
26
70618
4251
（笑声）
01:26
(Laughter)
27
74893
1869
我对成功的定义是：
01:28
My definition of success is:
28
76786
1845
如果我的孩子们肯吃蔬菜，
这顿饭就是成功的。
这顿饭就是成功的。
01:30
a meal is successful
if my kids eat vegetables.
if my kids eat vegetables.
29
78655
2659
01:34
It's very different
from if my youngest son were in charge.
from if my youngest son were in charge.
30
82181
2854
这和我最小的儿子
负责做饭时的情况有所不同。
负责做饭时的情况有所不同。
他说，如果他能吃很多
Nutella巧克力榛子酱就是成功。
Nutella巧克力榛子酱就是成功。
01:37
He'd say success is if
he gets to eat lots of Nutella.
he gets to eat lots of Nutella.
31
85059
2788
01:41
But I get to choose success.
32
89179
2226
但我可以选择成功。
我负责。我的意见就很重要。
01:43
I am in charge. My opinion matters.
33
91429
2707
这就是算法的第一个规则。
01:46
That's the first rule of algorithms.
34
94160
2675
算法是嵌入在代码中的观点。
01:48
Algorithms are opinions embedded in code.
35
96859
3180
01:53
It's really different from what you think
most people think of algorithms.
most people think of algorithms.
36
101562
3663
这和你认为大多数人对
算法的看法是不同的。
算法的看法是不同的。
他们认为算法是客观、真实和科学的。
01:57
They think algorithms are objective
and true and scientific.
and true and scientific.
37
105249
4504
02:02
That's a marketing trick.
38
110387
1699
那是一种营销技巧。
02:05
It's also a marketing trick
39
113269
2125
这也是一种用算法来
恐吓你的营销手段，
02:07
to intimidate you with algorithms,
40
115418
3154
为了让你信任和恐惧算法
02:10
to make you trust and fear algorithms
41
118596
3661
因为你信任并害怕数学。
02:14
because you trust and fear mathematics.
42
122281
2018
02:17
A lot can go wrong when we put
blind faith in big data.
blind faith in big data.
43
125567
4830
当我们盲目信任大数据时，
很多人都可能犯错。
很多人都可能犯错。
02:23
This is Kiri Soares.
She's a high school principal in Brooklyn.
She's a high school principal in Brooklyn.
44
131684
3373
这是凯丽·索尔斯。
她是布鲁克林的一名高中校长。
她是布鲁克林的一名高中校长。
2011年，她告诉我，
她学校的老师们正在被一个复杂
她学校的老师们正在被一个复杂
02:27
In 2011, she told me
her teachers were being scored
her teachers were being scored
45
135081
2586
02:29
with a complex, secret algorithm
46
137691
2727
并且隐秘的算法进行打分，
这个算法被称为“增值模型"。
02:32
called the "value-added model."
47
140442
1489
02:34
I told her, "Well, figure out
what the formula is, show it to me.
what the formula is, show it to me.
48
142505
3092
我告诉她，“先弄清楚这个
公式是什么，然后给我看看。
公式是什么，然后给我看看。
我来给你解释一下。”
02:37
I'm going to explain it to you."
49
145621
1541
她说，“我寻求过这个公式，
02:39
She said, "Well, I tried
to get the formula,
to get the formula,
50
147186
2141
但是教育部的负责人告诉我这是数学，
02:41
but my Department of Education contact
told me it was math
told me it was math
51
149351
2772
给我我也看不懂。”
02:44
and I wouldn't understand it."
52
152147
1546
02:47
It gets worse.
53
155266
1338
更糟的还在后面。
纽约邮报提出了“信息自由法”的要求，
02:48
The New York Post filed
a Freedom of Information Act request,
a Freedom of Information Act request,
54
156628
3530
来得到所有老师的名字与他们的分数，
02:52
got all the teachers' names
and all their scores
and all their scores
55
160182
2959
并且他们以羞辱教师的方式
发表了这些数据。
发表了这些数据。
02:55
and they published them
as an act of teacher-shaming.
as an act of teacher-shaming.
56
163165
2782
02:59
When I tried to get the formulas,
the source code, through the same means,
the source code, through the same means,
57
167084
3860
当我试图用同样的方法来获取公式，
源代码的时候，
源代码的时候，
03:02
I was told I couldn't.
58
170968
2149
我被告知我没有权力这么做。
我被拒绝了。
03:05
I was denied.
59
173141
1236
后来我发现，
03:06
I later found out
60
174401
1174
纽约市压根儿没有人能接触到这个公式。
03:07
that nobody in New York City
had access to that formula.
had access to that formula.
61
175599
2866
没有人能看懂。
03:10
No one understood it.
62
178489
1305
03:13
Then someone really smart
got involved, Gary Rubenstein.
got involved, Gary Rubenstein.
63
181929
3224
然后，一个非常聪明的人参与了，
加里·鲁宾斯坦。
加里·鲁宾斯坦。
03:17
He found 665 teachers
from that New York Post data
from that New York Post data
64
185177
3621
他从纽约邮报的数据中
找到了665名教师，
找到了665名教师，
03:20
that actually had two scores.
65
188822
1866
实际上他们有两个分数。
如果他们同时教七年级与八年级的数学，
03:22
That could happen if they were teaching
66
190712
1881
就会得到两个评分。
03:24
seventh grade math and eighth grade math.
67
192617
2439
他决定把这些数据绘成图表。
03:27
He decided to plot them.
68
195080
1538
每个点代表一个教师。
03:28
Each dot represents a teacher.
69
196642
1993
03:31
(Laughter)
70
199104
2379
（笑声）
03:33
What is that?
71
201507
1521
那是什么？
03:35
(Laughter)
72
203052
1277
（笑声）
03:36
That should never have been used
for individual assessment.
for individual assessment.
73
204353
3446
它永远不应该被用于个人评估。
它几乎是一个随机数生成器。
03:39
It's almost a random number generator.
74
207823
1926
03:41
(Applause)
75
209773
2946
（掌声）
但它确实被使用了。
03:44
But it was.
76
212743
1162
这是莎拉·维索斯基。
03:45
This is Sarah Wysocki.
77
213929
1176
她连同另外205名教师被解雇了，
03:47
She got fired, along
with 205 other teachers,
with 205 other teachers,
78
215129
2175
都是来自华盛顿特区的学区，
03:49
from the Washington, DC school district,
79
217328
2662
03:52
even though she had great
recommendations from her principal
recommendations from her principal
80
220014
2909
尽管她的校长还有学生的
父母都非常推荐她。
03:54
and the parents of her kids.
81
222947
1428
03:57
I know what a lot
of you guys are thinking,
of you guys are thinking,
82
225390
2032
我知道你们很多人在想什么，
尤其是这里的数据科学家，
人工智能专家。
人工智能专家。
03:59
especially the data scientists,
the AI experts here.
the AI experts here.
83
227446
2487
你在想，“我可永远不会做出
这样前后矛盾的算法。”
这样前后矛盾的算法。”
04:01
You're thinking, "Well, I would never make
an algorithm that inconsistent."
an algorithm that inconsistent."
84
229957
4226
04:06
But algorithms can go wrong,
85
234853
1683
但是算法可能会出错，
即使有良好的意图，
也会产生毁灭性的影响。
也会产生毁灭性的影响。
04:08
even have deeply destructive effects
with good intentions.
with good intentions.
86
236560
4598
04:14
And whereas an airplane
that's designed badly
that's designed badly
87
242531
2379
每个人都能看到一架设计的
很糟糕的飞机会坠毁在地，
04:16
crashes to the earth and everyone sees it,
88
244934
2001
而一个设计糟糕的算法
04:18
an algorithm designed badly
89
246959
1850
04:22
can go on for a long time,
silently wreaking havoc.
silently wreaking havoc.
90
250245
3865
可以持续很长一段时间，
并无声地造成破坏。
并无声地造成破坏。
04:27
This is Roger Ailes.
91
255748
1570
这是罗杰·艾尔斯。
04:29
(Laughter)
92
257342
2000
（笑声）
04:32
He founded Fox News in 1996.
93
260524
2388
他在1996年创办了福克斯新闻。
04:35
More than 20 women complained
about sexual harassment.
about sexual harassment.
94
263436
2581
公司有超过20多名女性曾抱怨过性骚扰。
她们说她们不被允许在
福克斯新闻有所成就。
福克斯新闻有所成就。
04:38
They said they weren't allowed
to succeed at Fox News.
to succeed at Fox News.
95
266041
3235
他去年被赶下台，但我们最近看到
04:41
He was ousted last year,
but we've seen recently
but we've seen recently
96
269300
2520
问题依然存在。
04:43
that the problems have persisted.
97
271844
2670
这引出了一个问题：
04:47
That begs the question:
98
275654
1400
福克斯新闻应该做些什么改变？
04:49
What should Fox News do
to turn over another leaf?
to turn over another leaf?
99
277078
2884
04:53
Well, what if they replaced
their hiring process
their hiring process
100
281245
3041
如果他们用机器学习算法
取代传统的招聘流程呢？
04:56
with a machine-learning algorithm?
101
284310
1654
听起来不错，对吧？
04:57
That sounds good, right?
102
285988
1595
想想看。
04:59
Think about it.
103
287607
1300
数据，这些数据到底是什么？
05:00
The data, what would the data be?
104
288931
2105
福克斯新闻在过去21年的申请函
是一个合理的选择。
是一个合理的选择。
05:03
A reasonable choice would be the last
21 years of applications to Fox News.
21 years of applications to Fox News.
105
291060
4947
很合理。
05:08
Reasonable.
106
296031
1502
那么成功的定义呢？
05:09
What about the definition of success?
107
297557
1938
合理的选择将是，
05:11
Reasonable choice would be,
108
299921
1324
谁在福克斯新闻取得了成功？
05:13
well, who is successful at Fox News?
109
301269
1778
我猜的是，比如在那里呆了四年，
05:15
I guess someone who, say,
stayed there for four years
stayed there for four years
110
303071
3580
至少得到过一次晋升的人。
05:18
and was promoted at least once.
111
306675
1654
05:20
Sounds reasonable.
112
308816
1561
听起来很合理。
05:22
And then the algorithm would be trained.
113
310401
2354
然后这个算法将会被训练。
它会被训练去向人们
学习是什么造就了成功，
学习是什么造就了成功，
05:24
It would be trained to look for people
to learn what led to success,
to learn what led to success,
114
312779
3877
05:29
what kind of applications
historically led to success
historically led to success
115
317219
4318
什么样的申请函在过去拥有
这种成功的定义。
05:33
by that definition.
116
321561
1294
05:36
Now think about what would happen
117
324200
1775
现在想想如果我们把它
应用到目前的申请者中会发生什么。
05:37
if we applied that
to a current pool of applicants.
to a current pool of applicants.
118
325999
2555
它会过滤掉女性，
05:41
It would filter out women
119
329119
1629
05:43
because they do not look like people
who were successful in the past.
who were successful in the past.
120
331663
3930
因为她们看起来不像
在过去取得成功的人。
在过去取得成功的人。
05:51
Algorithms don't make things fair
121
339752
2537
算法不会让事情变得公平，
如果你只是轻率地，
盲目地应用算法。
盲目地应用算法。
05:54
if you just blithely,
blindly apply algorithms.
blindly apply algorithms.
122
342313
2694
它们不会让事情变得公平。
05:57
They don't make things fair.
123
345031
1482
它们只是重复我们过去的做法，
05:58
They repeat our past practices,
124
346537
2128
06:00
our patterns.
125
348689
1183
我们的规律。
它们使现状自动化。
06:01
They automate the status quo.
126
349896
1939
06:04
That would be great
if we had a perfect world,
if we had a perfect world,
127
352718
2389
如果我们有一个
完美的世界那就太好了，
完美的世界那就太好了，
06:07
but we don't.
128
355905
1312
但是我们没有。
我还要补充一点，
大多数公司都没有令人尴尬的诉讼，
大多数公司都没有令人尴尬的诉讼，
06:09
And I'll add that most companies
don't have embarrassing lawsuits,
don't have embarrassing lawsuits,
129
357241
4102
06:14
but the data scientists in those companies
130
362446
2588
但是这些公司的数据科学家
06:17
are told to follow the data,
131
365058
2189
被告知要跟随数据，
关注它的准确性。
06:19
to focus on accuracy.
132
367271
2143
06:22
Think about what that means.
133
370273
1381
想想这意味着什么。
因为我们都有偏见，
这意味着他们可以编纂性别歧视
这意味着他们可以编纂性别歧视
06:23
Because we all have bias,
it means they could be codifying sexism
it means they could be codifying sexism
134
371678
4027
或者任何其他的偏见。
06:27
or any other kind of bigotry.
135
375729
1836
06:31
Thought experiment,
136
379488
1421
思维实验，
因为我喜欢它们：
06:32
because I like them:
137
380933
1509
06:35
an entirely segregated society --
138
383574
2975
一个完全隔离的社会——
06:40
racially segregated, all towns,
all neighborhoods
all neighborhoods
139
388247
3328
种族隔离存在于所有的城镇，
所有的社区，
所有的社区，
我们把警察只送到少数族裔的社区
06:43
and where we send the police
only to the minority neighborhoods
only to the minority neighborhoods
140
391599
3037
去寻找犯罪。
06:46
to look for crime.
141
394660
1193
06:48
The arrest data would be very biased.
142
396451
2219
逮捕数据将会是十分有偏见的。
06:51
What if, on top of that,
we found the data scientists
we found the data scientists
143
399851
2575
除此之外，我们还会寻找数据科学家
并付钱给他们来预测
下一起犯罪会发生在哪里？
下一起犯罪会发生在哪里？
06:54
and paid the data scientists to predict
where the next crime would occur?
where the next crime would occur?
144
402450
4161
06:59
Minority neighborhood.
145
407275
1487
少数族裔的社区。
07:01
Or to predict who the next
criminal would be?
criminal would be?
146
409285
3125
或者预测下一个罪犯会是谁？
07:04
A minority.
147
412888
1395
少数族裔。
07:07
The data scientists would brag
about how great and how accurate
about how great and how accurate
148
415949
3541
这些数据科学家们
会吹嘘他们的模型有多好，
会吹嘘他们的模型有多好，
多精确，
07:11
their model would be,
149
419514
1297
07:12
and they'd be right.
150
420835
1299
当然他们是对的。
07:15
Now, reality isn't that drastic,
but we do have severe segregations
but we do have severe segregations
151
423951
4615
不过现实并没有那么极端，
但我们确实在许多城市里
但我们确实在许多城市里
有严重的种族隔离，
07:20
in many cities and towns,
152
428590
1287
并且我们有大量的证据表明
07:21
and we have plenty of evidence
153
429901
1893
07:23
of biased policing
and justice system data.
and justice system data.
154
431818
2688
警察和司法系统的数据存有偏见。
07:27
And we actually do predict hotspots,
155
435632
2815
而且我们确实预测过热点，
07:30
places where crimes will occur.
156
438471
1530
那些犯罪会发生的地方。
07:32
And we do predict, in fact,
the individual criminality,
the individual criminality,
157
440401
3866
我们确实会预测个人犯罪，
个人的犯罪行为。
07:36
the criminality of individuals.
158
444291
1770
07:38
The news organization ProPublica
recently looked into
recently looked into
159
446972
3963
新闻机构“人民 (ProPublica)”最近调查了，
其中一个称为
07:42
one of those "recidivism risk" algorithms,
160
450959
2024
“累犯风险”的算法。
07:45
as they're called,
161
453007
1163
并在佛罗里达州的
宣判期间被法官采用。
宣判期间被法官采用。
07:46
being used in Florida
during sentencing by judges.
during sentencing by judges.
162
454194
3194
07:50
Bernard, on the left, the black man,
was scored a 10 out of 10.
was scored a 10 out of 10.
163
458411
3585
伯纳德，左边的那个黑人，
10分中得了满分。
10分中得了满分。
07:55
Dylan, on the right, 3 out of 10.
164
463179
2007
在右边的迪伦，
10分中得了3分。
10分中得了3分。
10分代表高风险。
3分代表低风险。
3分代表低风险。
07:57
10 out of 10, high risk.
3 out of 10, low risk.
3 out of 10, low risk.
165
465210
2501
08:00
They were both brought in
for drug possession.
for drug possession.
166
468598
2385
他们都因为持有毒品
而被带进了监狱。
而被带进了监狱。
他们都有犯罪记录，
08:03
They both had records,
167
471007
1154
08:04
but Dylan had a felony
168
472185
2806
但是迪伦有一个重罪
但伯纳德没有。
08:07
but Bernard didn't.
169
475015
1176
08:09
This matters, because
the higher score you are,
the higher score you are,
170
477818
3066
这很重要，因为你的分数越高，
你被判长期服刑的可能性就越大。
08:12
the more likely you're being given
a longer sentence.
a longer sentence.
171
480908
3473
08:18
What's going on?
172
486294
1294
到底发生了什么？
08:20
Data laundering.
173
488526
1332
数据洗钱。
08:22
It's a process by which
technologists hide ugly truths
technologists hide ugly truths
174
490930
4427
这是一个技术人员
把丑陋真相隐藏在
把丑陋真相隐藏在
算法黑盒子中的过程，
08:27
inside black box algorithms
175
495381
1821
并称之为客观；
08:29
and call them objective;
176
497226
1290
08:31
call them meritocratic.
177
499320
1568
称之为精英模式。
08:35
When they're secret,
important and destructive,
important and destructive,
178
503118
2385
当它们是秘密的，
重要的并具有破坏性的，
重要的并具有破坏性的，
我为这些算法创造了一个术语：
08:37
I've coined a term for these algorithms:
179
505527
2487
08:40
"weapons of math destruction."
180
508038
1999
“杀伤性数学武器”。
08:42
(Laughter)
181
510061
1564
（笑声）
08:43
(Applause)
182
511649
3054
（鼓掌）
它们无处不在，也不是一个错误。
08:46
They're everywhere,
and it's not a mistake.
and it's not a mistake.
183
514727
2354
08:49
These are private companies
building private algorithms
building private algorithms
184
517695
3723
这些是私有公司为了私人目的
建立的私有算法。
08:53
for private ends.
185
521442
1392
08:55
Even the ones I talked about
for teachers and the public police,
for teachers and the public police,
186
523214
3214
甚至是我谈到的老师
与公共警察使用的（算法），
与公共警察使用的（算法），
08:58
those were built by private companies
187
526452
1869
也都是由私人公司所打造的，
然后卖给政府机构。
09:00
and sold to the government institutions.
188
528345
2231
09:02
They call it their "secret sauce" --
189
530600
1873
他们称之为“秘密配方（来源）”——
这就是他们不能告诉我们的原因。
09:04
that's why they can't tell us about it.
190
532497
2128
09:06
It's also private power.
191
534649
2220
这也是私人权力。
09:09
They are profiting for wielding
the authority of the inscrutable.
the authority of the inscrutable.
192
537924
4695
他们利用神秘莫测的权威来获利。
09:17
Now you might think,
since all this stuff is private
since all this stuff is private
193
545114
2934
你可能会想，既然所有这些都是私有的
而且会有竞争，
09:20
and there's competition,
194
548072
1158
也许自由市场会解决这个问题。
09:21
maybe the free market
will solve this problem.
will solve this problem.
195
549254
2306
然而并不会。
09:23
It won't.
196
551584
1249
在不公平的情况下，
有很多钱可以赚。
有很多钱可以赚。
09:24
There's a lot of money
to be made in unfairness.
to be made in unfairness.
197
552857
3120
09:29
Also, we're not economic rational agents.
198
557127
3369
而且，我们不是经济理性的代理人。
09:33
We all are biased.
199
561031
1292
我们都是有偏见的。
09:34
We're all racist and bigoted
in ways that we wish we weren't,
in ways that we wish we weren't,
200
562960
3377
我们都是固执的种族主义者，
虽然我们希望我们不是，
虽然我们希望我们不是，
虽然我们甚至没有意识到。
09:38
in ways that we don't even know.
201
566361
2019
09:41
We know this, though, in aggregate,
202
569352
3081
总的来说，我们知道这一点，
因为社会学家会一直通过这些实验
09:44
because sociologists
have consistently demonstrated this
have consistently demonstrated this
203
572457
3220
来证明这一点，
09:47
with these experiments they build,
204
575701
1665
他们发送了大量的工作申请，
09:49
where they send a bunch
of applications to jobs out,
of applications to jobs out,
205
577390
2568
都是有同样资格的候选人，
有些用白人人名，
有些用白人人名，
09:51
equally qualified but some
have white-sounding names
have white-sounding names
206
579982
2501
有些用黑人人名，
09:54
and some have black-sounding names,
207
582507
1706
然而结果总是令人失望的。
09:56
and it's always disappointing,
the results -- always.
the results -- always.
208
584237
2694
09:59
So we are the ones that are biased,
209
587510
1771
所以我们是有偏见的，
我们还通过选择收集到的数据
10:01
and we are injecting those biases
into the algorithms
into the algorithms
210
589305
3429
10:04
by choosing what data to collect,
211
592758
1812
来把偏见注入到算法中，
10:06
like I chose not to think
about ramen noodles --
about ramen noodles --
212
594594
2743
就像我不选择去想拉面一样——
我自认为这无关紧要。
10:09
I decided it was irrelevant.
213
597361
1625
但是，通过信任那些
在过去的实践中获得的数据
在过去的实践中获得的数据
10:11
But by trusting the data that's actually
picking up on past practices
picking up on past practices
214
599010
5684
10:16
and by choosing the definition of success,
215
604718
2014
以及通过选择成功的定义，
10:18
how can we expect the algorithms
to emerge unscathed?
to emerge unscathed?
216
606756
3983
我们怎么能指望算法
会是毫无瑕疵的呢？
会是毫无瑕疵的呢？
我们不能。我们必须检查。
10:22
We can't. We have to check them.
217
610763
2356
10:26
We have to check them for fairness.
218
614165
1709
我们必须检查它们是否公平。
好消息是，我们可以做到这一点。
10:27
The good news is,
we can check them for fairness.
we can check them for fairness.
219
615898
2711
算法是可以被审问的，
10:30
Algorithms can be interrogated,
220
618633
3352
而且每次都能告诉我们真相。
10:34
and they will tell us
the truth every time.
the truth every time.
221
622009
2034
10:36
And we can fix them.
We can make them better.
We can make them better.
222
624067
2493
然后我们可以修复它们。
我们可以让他们变得更好。
我们可以让他们变得更好。
我把它叫做算法审计，
10:38
I call this an algorithmic audit,
223
626584
2375
接下来我会为你们解释。
10:40
and I'll walk you through it.
224
628983
1679
10:42
First, data integrity check.
225
630686
2196
首先，数据的完整性检查。
10:46
For the recidivism risk
algorithm I talked about,
algorithm I talked about,
226
634132
2657
对于刚才提到过的累犯风险算法，
10:49
a data integrity check would mean
we'd have to come to terms with the fact
we'd have to come to terms with the fact
227
637582
3573
数据的完整性检查将意味着
我们不得不接受这个事实，
我们不得不接受这个事实，
10:53
that in the US, whites and blacks
smoke pot at the same rate
smoke pot at the same rate
228
641179
3526
在美国，白人和黑人
吸毒的比例是一样的，
吸毒的比例是一样的，
但是黑人更有可能被逮捕——
10:56
but blacks are far more likely
to be arrested --
to be arrested --
229
644729
2485
10:59
four or five times more likely,
depending on the area.
depending on the area.
230
647238
3184
取决于区域，可能性是白人的4到5倍。
11:03
What is that bias looking like
in other crime categories,
in other crime categories,
231
651317
2826
这种偏见在其他犯罪类别中
是什么样子的，
是什么样子的，
11:06
and how do we account for it?
232
654167
1451
我们又该如何解释呢？
11:08
Second, we should think about
the definition of success,
the definition of success,
233
656162
3039
其次，我们应该考虑成功的定义，
审计它。
11:11
audit that.
234
659225
1381
还记得我们谈论的雇佣算法吗？
11:12
Remember -- with the hiring
algorithm? We talked about it.
algorithm? We talked about it.
235
660630
2752
那个呆了四年的人，
然后被提升了一次？
然后被提升了一次？
11:15
Someone who stays for four years
and is promoted once?
and is promoted once?
236
663406
3165
11:18
Well, that is a successful employee,
237
666595
1769
这的确是一个成功的员工，
但这也是一名受到公司文化支持的员工。
11:20
but it's also an employee
that is supported by their culture.
that is supported by their culture.
238
668388
3079
11:24
That said, also it can be quite biased.
239
672089
1926
也就是说，
这可能会有很大的偏差。
这可能会有很大的偏差。
我们需要把这两件事分开。
11:26
We need to separate those two things.
240
674039
2065
我们应该去看一下乐团盲选试奏，
11:28
We should look to
the blind orchestra audition
the blind orchestra audition
241
676128
2426
举个例子。
11:30
as an example.
242
678578
1196
这就是人们在幕后选拔乐手的地方。
11:31
That's where the people auditioning
are behind a sheet.
are behind a sheet.
243
679798
2756
11:34
What I want to think about there
244
682946
1931
我想要考虑的是
倾听的人已经
决定了什么是重要的，
决定了什么是重要的，
11:36
is the people who are listening
have decided what's important
have decided what's important
245
684901
3417
11:40
and they've decided what's not important,
246
688342
2029
同时他们已经决定了
什么是不重要的，
什么是不重要的，
他们也不会因此而分心。
11:42
and they're not getting
distracted by that.
distracted by that.
247
690395
2059
11:44
When the blind orchestra
auditions started,
auditions started,
248
692961
2749
当乐团盲选开始时，
在管弦乐队中，
女性的数量上升了5倍。
女性的数量上升了5倍。
11:47
the number of women in orchestras
went up by a factor of five.
went up by a factor of five.
249
695734
3444
11:52
Next, we have to consider accuracy.
250
700253
2015
其次，我们必须考虑准确性。
11:55
This is where the value-added model
for teachers would fail immediately.
for teachers would fail immediately.
251
703233
3734
这就是针对教师的增值模型
立刻失效的地方。
立刻失效的地方。
11:59
No algorithm is perfect, of course,
252
707578
2162
当然，没有一个算法是完美的，
12:02
so we have to consider
the errors of every algorithm.
the errors of every algorithm.
253
710620
3605
所以我们要考虑每一个算法的误差。
12:06
How often are there errors,
and for whom does this model fail?
and for whom does this model fail?
254
714836
4359
出现错误的频率有多高，
让这个模型失败的对象是谁？
让这个模型失败的对象是谁？
12:11
What is the cost of that failure?
255
719850
1718
失败的代价是什么？
12:14
And finally, we have to consider
256
722434
2207
最后，我们必须考虑
12:17
the long-term effects of algorithms,
257
725973
2186
这个算法的长期效果，
12:20
the feedback loops that are engendering.
258
728866
2207
与正在产生的反馈循环。
12:23
That sounds abstract,
259
731586
1236
这听起来很抽象，
但是想象一下
如果脸书的工程师们之前考虑过，
如果脸书的工程师们之前考虑过，
12:24
but imagine if Facebook engineers
had considered that
had considered that
260
732846
2664
12:28
before they decided to show us
only things that our friends had posted.
only things that our friends had posted.
261
736270
4855
并决定只向我们展示
我们朋友所发布的东西。
我们朋友所发布的东西。
12:33
I have two more messages,
one for the data scientists out there.
one for the data scientists out there.
262
741761
3234
我还有两条建议，
一条是给数据科学家的。
一条是给数据科学家的。
12:37
Data scientists: we should
not be the arbiters of truth.
not be the arbiters of truth.
263
745450
3409
数据科学家们：我们不应该
成为真相的仲裁者。
成为真相的仲裁者。
12:41
We should be translators
of ethical discussions that happen
of ethical discussions that happen
264
749520
3783
我们应该成为大社会中
所发生的道德讨论的
所发生的道德讨论的
翻译者。
12:45
in larger society.
265
753327
1294
12:47
(Applause)
266
755579
2133
（掌声）
12:49
And the rest of you,
267
757736
1556
然后剩下的人，
12:52
the non-data scientists:
268
760011
1396
非数据科学家们：
这不是一个数学测试。
12:53
this is not a math test.
269
761431
1498
12:55
This is a political fight.
270
763632
1348
这是一场政治斗争。
12:58
We need to demand accountability
for our algorithmic overlords.
for our algorithmic overlords.
271
766587
3907
我们应该要求我们的
算法霸主承担问责。
算法霸主承担问责。
13:04
(Applause)
272
772118
1499
（掌声）
13:05
The era of blind faith
in big data must end.
in big data must end.
273
773641
4225
盲目信仰大数据的时代必须结束。
非常感谢。
13:09
Thank you very much.
274
777890
1167
（掌声）
13:11
(Applause)
275
779081
5303
Translated by Lin Zhang
ABOUT THE SPEAKER
Cathy O'Neil - Mathematician, data scientistData skeptic Cathy O’Neil uncovers the dark secrets of big data, showing how our "objective" algorithms could in fact reinforce human bias.
Why you should listen
In 2008, as a hedge-fund quant, mathematician Cathy O’Neil saw firsthand how really really bad math could lead to financial disaster. Disillusioned, O’Neil became a data scientist and eventually joined Occupy Wall Street’s Alternative Banking Group.
With her popular blog mathbabe.org, O’Neil emerged as an investigative journalist. Her acclaimed book Weapons of Math Destruction details how opaque, black-box algorithms rely on biased historical data to do everything from sentence defendants to hire workers. In 2017, O’Neil founded consulting firm ORCAA to audit algorithms for racial, gender and economic inequality.
Cathy O'Neil | Speaker | TED.com