TEDxWarwick
Kriti Sharma: How to keep human bias out of AI
克里蒂 • 夏尔马: 如何让人工智能远离人类的偏见
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人工智能算法一直在对你做出重要的决定——比如你应该支付多少车险，或者你是否应该得到面试机会。但是，当这些机器被植入人类的偏见时，会发生什么呢？技术专家克里蒂•夏尔马(Kriti Sharma)探讨了技术缺乏多样性是如何渗透到我们的人工智能中的。她提供了三种方法，让我们可以开始设计更合乎道德的算法。
Kriti Sharma - AI technologist
Kriti Sharma creates AI technology to help address some of the toughest social challenges of our time -- from domestic violence to sexual health and inequality. Full bio
Kriti Sharma creates AI technology to help address some of the toughest social challenges of our time -- from domestic violence to sexual health and inequality. Full bio
Double-click the English transcript below to play the video.
00:12
How many decisions
have been made about you today,
have been made about you today,
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你今天，这周，或今年
有多少决定
00:16
or this week or this year,
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是人工智能（AI）做出的？
00:19
by artificial intelligence?
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00:22
I build AI for a living
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我靠创建AI为生，
所以，坦白说，我是个技术狂。
00:24
so, full disclosure, I'm kind of a nerd.
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因为我是算是个技术狂，
00:27
And because I'm kind of a nerd,
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每当有关于人工智能
要抢走我们的工作
要抢走我们的工作
00:30
wherever some new news story comes out
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00:32
about artificial intelligence
stealing all our jobs,
stealing all our jobs,
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这样的新闻报道出来，
或者机器人获得了
一个国家的公民身份时，
一个国家的公民身份时，
00:35
or robots getting citizenship
of an actual country,
of an actual country,
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我就成了对未来感到
担忧的朋友和关注者
担忧的朋友和关注者
00:40
I'm the person my friends
and followers message
and followers message
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发消息的对象。
00:43
freaking out about the future.
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00:45
We see this everywhere.
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这种事情随处可见。
媒体担心机器人
正在接管人类的统治。
正在接管人类的统治。
00:47
This media panic that
our robot overlords are taking over.
our robot overlords are taking over.
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我们可以为此谴责好莱坞。
00:52
We could blame Hollywood for that.
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00:56
But in reality, that's not the problem
we should be focusing on.
we should be focusing on.
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但现实中，这不是
我们应该关注的问题。
我们应该关注的问题。
01:01
There is a more pressing danger,
a bigger risk with AI,
a bigger risk with AI,
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人工智能还有一个更紧迫的
危机， 一个更大的风险，
危机， 一个更大的风险，
需要我们首先应对。
01:04
that we need to fix first.
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01:07
So we are back to this question:
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所以我们再回到这个问题：
今天我们有多少决定
是由人工智能做出的？
是由人工智能做出的？
01:09
How many decisions
have been made about you today by AI?
have been made about you today by AI?
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01:15
And how many of these
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其中有多少决定
是基于你的性别，种族或者背景？
01:17
were based on your gender,
your race or your background?
your race or your background?
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01:24
Algorithms are being used all the time
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算法一直在被用来
判断我们是谁，我们想要什么。
01:27
to make decisions about who we are
and what we want.
and what we want.
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01:32
Some of the women in this room
will know what I'm talking about
will know what I'm talking about
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在座的人里有些女性
知道我在说什么，
知道我在说什么，
如果你有上千次
被要求看完YouTube上
被要求看完YouTube上
01:35
if you've been made to sit through
those pregnancy test adverts on YouTube
those pregnancy test adverts on YouTube
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那些怀孕测试广告，
01:39
like 1,000 times.
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或者你在Faceboo的短新闻中
01:41
Or you've scrolled past adverts
of fertility clinics
of fertility clinics
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刷到过生育诊所的广告。
01:44
on your Facebook feed.
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01:47
Or in my case, Indian marriage bureaus.
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或者我的遇到的情况是，
印度婚姻局。
印度婚姻局。
（笑声）
01:50
(Laughter)
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但人工智能不仅被用来决定
01:51
But AI isn't just being used
to make decisions
to make decisions
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我们想要买什么产品，
01:54
about what products we want to buy
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或者我们接下来想刷哪部剧。
01:56
or which show we want to binge watch next.
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02:01
I wonder how you'd feel about someone
who thought things like this:
who thought things like this:
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我想知道你会怎么看这样想的人：
“黑人或拉丁美洲人
02:06
"A black or Latino person
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比白人更不可能按时还贷。”
02:08
is less likely than a white person
to pay off their loan on time."
to pay off their loan on time."
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02:13
"A person called John
makes a better programmer
makes a better programmer
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“名叫约翰的人编程能力
要比叫玛丽的人好。”
02:16
than a person called Mary."
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02:19
"A black man is more likely to be
a repeat offender than a white man."
a repeat offender than a white man."
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“黑人比白人更有可能成为惯犯。”
02:26
You're probably thinking,
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你可能在想，
“哇，这听起来像是一个有严重
性别歧视和种族歧视的人。” 对吧？
性别歧视和种族歧视的人。” 对吧？
02:28
"Wow, that sounds like a pretty sexist,
racist person," right?
racist person," right?
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02:33
These are some real decisions
that AI has made very recently,
that AI has made very recently,
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这些都是人工智能
近期做出的真实决定，
近期做出的真实决定，
基于它从我们人类身上
02:37
based on the biases
it has learned from us,
it has learned from us,
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学习到的偏见。
02:40
from the humans.
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02:43
AI is being used to help decide
whether or not you get that job interview;
whether or not you get that job interview;
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人工智能被用来帮助决定
你是否能够得到面试机会；
你是否能够得到面试机会；
02:48
how much you pay for your car insurance;
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你应该为车险支付多少费用；
你的信用分数有多好；
02:51
how good your credit score is;
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甚至你在年度绩效评估中
应该得到怎样的评分。
应该得到怎样的评分。
02:52
and even what rating you get
in your annual performance review.
in your annual performance review.
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02:57
But these decisions
are all being filtered through
are all being filtered through
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但这些决定都是
通过它对我们的身份、
通过它对我们的身份、
种族、性别和年龄的
假设过滤出来的。
假设过滤出来的。
03:00
its assumptions about our identity,
our race, our gender, our age.
our race, our gender, our age.
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03:08
How is that happening?
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为什么会这样？
想象一下人工智能
正在帮助一个人事主管
正在帮助一个人事主管
03:10
Now, imagine an AI is helping
a hiring manager
a hiring manager
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寻找公司下一位科技领袖。
03:14
find the next tech leader in the company.
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目前为止，主管雇佣的大部分是男性。
03:16
So far, the manager
has been hiring mostly men.
has been hiring mostly men.
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所以人工智能知道男人比女人
更有可能成为程序员，
更有可能成为程序员，
03:20
So the AI learns men are more likely
to be programmers than women.
to be programmers than women.
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03:25
And it's a very short leap from there to:
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也就更容易做出这样的判断：
男人比女人更擅长编程。
03:28
men make better programmers than women.
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03:31
We have reinforced
our own bias into the AI.
our own bias into the AI.
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我们通过人工智能强化了自己的偏见。
现在，它正在筛选掉女性候选人。
03:35
And now, it's screening out
female candidates.
female candidates.
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03:40
Hang on, if a human
hiring manager did that,
hiring manager did that,
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等等，如果人类招聘主管这样做，
我们会很愤怒，
不允许这样的事情发生。
不允许这样的事情发生。
03:43
we'd be outraged, we wouldn't allow it.
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这种性别偏见让人难以接受。
03:46
This kind of gender
discrimination is not OK.
discrimination is not OK.
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然而，或多或少，
人工智能已经凌驾于法律之上，
人工智能已经凌驾于法律之上，
03:49
And yet somehow,
AI has become above the law,
AI has become above the law,
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因为是机器做的决定。
03:54
because a machine made the decision.
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03:57
That's not it.
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这还没完。
我们也在强化我们与
人工智能互动的偏见。
人工智能互动的偏见。
03:59
We are also reinforcing our bias
in how we interact with AI.
in how we interact with AI.
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04:04
How often do you use a voice assistant
like Siri, Alexa or even Cortana?
like Siri, Alexa or even Cortana?
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你们使用Siri，Alexa或者Cortana
这样的语音助手有多频繁？
这样的语音助手有多频繁？
它们有两点是相同的：
04:10
They all have two things in common:
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第一点，它们总是搞错我的名字，
04:13
one, they can never get my name right,
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第二点，它们都有女性特征。
04:16
and second, they are all female.
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04:20
They are designed to be
our obedient servants,
our obedient servants,
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它们都被设计成顺从我们的仆人，
开灯关灯，下单购买商品。
04:23
turning your lights on and off,
ordering your shopping.
ordering your shopping.
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04:27
You get male AIs too,
but they tend to be more high-powered,
but they tend to be more high-powered,
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也有男性的人工智能，
但他们倾向于拥有更高的权力，
但他们倾向于拥有更高的权力，
04:30
like IBM Watson,
making business decisions,
making business decisions,
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比如IBM的Watson可以做出商业决定，
还有Salesforce的Einstein
或者ROSS, 是机器人律师。
或者ROSS, 是机器人律师。
04:33
Salesforce Einstein
or ROSS, the robot lawyer.
or ROSS, the robot lawyer.
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04:38
So poor robots, even they suffer
from sexism in the workplace.
from sexism in the workplace.
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所以即便是机器人也没能
逃脱工作中的性别歧视。
逃脱工作中的性别歧视。
04:42
(Laughter)
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（笑声）
04:44
Think about how these two things combine
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想想这两者如何结合在一起，
04:47
and affect a kid growing up
in today's world around AI.
in today's world around AI.
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又会影响一个在当今人工智能
世界中长大的孩子。
世界中长大的孩子。
比如他们正在为学校的
一个项目做一些研究，
一个项目做一些研究，
04:52
So they're doing some research
for a school project
for a school project
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他们在谷歌上搜索了CEO的照片。
04:55
and they Google images of CEO.
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算法向他们展示的大部分是男性。
04:58
The algorithm shows them
results of mostly men.
results of mostly men.
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他们又搜索了个人助手。
05:01
And now, they Google personal assistant.
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你可以猜到，它显示的大部分是女性。
05:04
As you can guess,
it shows them mostly females.
it shows them mostly females.
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然后他们想放点音乐，
也许想点些吃的，
也许想点些吃的，
05:07
And then they want to put on some music,
and maybe order some food,
and maybe order some food,
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而现在，他们正对着一位
顺从的女声助手发号施令。
顺从的女声助手发号施令。
05:11
and now, they are barking orders
at an obedient female voice assistant.
at an obedient female voice assistant.
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05:19
Some of our brightest minds
are creating this technology today.
are creating this technology today.
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我们中一些最聪明的人
创建了今天的这个技术。
创建了今天的这个技术。
他们可以用任何他们
想要的方式创造技术。
想要的方式创造技术。
05:24
Technology that they could have created
in any way they wanted.
in any way they wanted.
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然而，他们却选择了上世纪50年代
《广告狂人》的秘书风格。
《广告狂人》的秘书风格。
05:29
And yet, they have chosen to create it
in the style of 1950s "Mad Man" secretary.
in the style of 1950s "Mad Man" secretary.
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是的，你没听错！
05:34
Yay!
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05:36
But OK, don't worry,
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但还好，不用担心。
这不会因为我告诉你
05:38
this is not going to end
with me telling you
with me telling you
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我们都在朝着性别歧视、
种族主义的机器前进而结束。
种族主义的机器前进而结束。
05:40
that we are all heading towards
sexist, racist machines running the world.
sexist, racist machines running the world.
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05:44
The good news about AI
is that it is entirely within our control.
is that it is entirely within our control.
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人工智能的好处是，
一切都在我们的控制中。
一切都在我们的控制中。
05:51
We get to teach the right values,
the right ethics to AI.
the right ethics to AI.
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我们得告诉人工智能
正确的价值观，道德观。
正确的价值观，道德观。
05:56
So there are three things we can do.
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所以有三件事我们可以做。
第一，我们能够意识到自己的偏见
05:58
One, we can be aware of our own biases
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和我们身边机器的偏见。
06:01
and the bias in machines around us.
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第二，我们可以确保打造
这个技术的是背景多样的团队。
这个技术的是背景多样的团队。
06:04
Two, we can make sure that diverse teams
are building this technology.
are building this technology.
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第三，我们必须让它
从丰富的经验中学习。
从丰富的经验中学习。
06:09
And three, we have to give it
diverse experiences to learn from.
diverse experiences to learn from.
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06:14
I can talk about the first two
from personal experience.
from personal experience.
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我可以从我个人的经验来说明前两点。
当你在科技行业工作，
06:18
When you work in technology
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06:19
and you don't look like
a Mark Zuckerberg or Elon Musk,
a Mark Zuckerberg or Elon Musk,
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并且不像马克·扎克伯格
或埃隆·马斯克那样位高权重，
或埃隆·马斯克那样位高权重，
你的生活会有点困难，
你的能力会收到质疑。
你的能力会收到质疑。
06:23
your life is a little bit difficult,
your ability gets questioned.
your ability gets questioned.
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06:27
Here's just one example.
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这只是一个例子。
跟大部分开发者一样，
我经常参加在线科技论坛，
我经常参加在线科技论坛，
06:29
Like most developers,
I often join online tech forums
I often join online tech forums
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分享我的知识帮助别人。
06:33
and share my knowledge to help others.
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我发现，
06:36
And I've found,
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当我用自己的照片，
自己的名字登陆时，
自己的名字登陆时，
06:37
when I log on as myself,
with my own photo, my own name,
with my own photo, my own name,
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我倾向于得到这样的问题或评论：
06:41
I tend to get questions
or comments like this:
or comments like this:
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“你为什么觉得自己
有资格谈论人工智能？”
有资格谈论人工智能？”
06:46
"What makes you think
you're qualified to talk about AI?"
you're qualified to talk about AI?"
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06:50
"What makes you think
you know about machine learning?"
you know about machine learning?"
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“你为什么觉得你了解机器学习？”
所以，我创建了新的资料页，
06:53
So, as you do, I made a new profile,
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这次，我没有选择自己的照片，
而是选择了一只带着喷气背包的猫。
而是选择了一只带着喷气背包的猫。
06:57
and this time, instead of my own picture,
I chose a cat with a jet pack on it.
I chose a cat with a jet pack on it.
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并选择了一个无法体现我性别的名字。
07:02
And I chose a name
that did not reveal my gender.
that did not reveal my gender.
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07:05
You can probably guess
where this is going, right?
where this is going, right?
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你能够大概猜到会怎么样，对吧？
于是这次，我不再收到
任何居高临下的评论，
任何居高临下的评论，
07:08
So, this time, I didn't get any of those
patronizing comments about my ability
patronizing comments about my ability
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我能够专心把工作做完。
07:15
and I was able to actually
get some work done.
get some work done.
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07:19
And it sucks, guys.
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这感觉太糟糕了，伙计们。
我从15岁起就在构建机器人，
07:21
I've been building robots since I was 15,
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我有计算机科学领域的几个学位，
07:23
I have a few degrees in computer science,
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然而，我不得不隐藏我的性别
07:26
and yet, I had to hide my gender
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以让我的工作被严肃对待。
07:28
in order for my work
to be taken seriously.
to be taken seriously.
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07:31
So, what's going on here?
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这是怎么回事呢？
男性在科技领域就是强于女性吗？
07:33
Are men just better
at technology than women?
at technology than women?
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07:37
Another study found
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另一个研究发现，
当女性程序员在平台上
隐藏性别时，像我这样，
隐藏性别时，像我这样，
07:39
that when women coders on one platform
hid their gender, like myself,
hid their gender, like myself,
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她们的代码被接受的
比例比男性高4%。
比例比男性高4%。
07:44
their code was accepted
four percent more than men.
four percent more than men.
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07:48
So this is not about the talent.
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所以这跟能力无关。
07:51
This is about an elitism in AI
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这是人工智能领域的精英主义，
即程序员看起来得像
具备某个特征的人。
具备某个特征的人。
07:54
that says a programmer
needs to look like a certain person.
needs to look like a certain person.
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07:59
What we really need to do
to make AI better
to make AI better
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让人工智能变得更好，
我们需要切实的
我们需要切实的
把来自不同背景的人集合到一起。
08:02
is bring people
from all kinds of backgrounds.
from all kinds of backgrounds.
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08:06
We need people who can
write and tell stories
write and tell stories
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我们需要能够书写和讲故事的人
来帮助我们创建人工智能更好的个性。
08:09
to help us create personalities of AI.
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08:12
We need people who can solve problems.
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我们需要能够解决问题的人。
08:15
We need people
who face different challenges
who face different challenges
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我们需要能应对不同挑战的人，
我们需要有人告诉我们什么是
真正需要解决的问题，
真正需要解决的问题，
08:18
and we need people who can tell us
what are the real issues that need fixing
what are the real issues that need fixing
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帮助我们找到用技术
解决问题的方法。
解决问题的方法。
08:24
and help us find ways
that technology can actually fix it.
that technology can actually fix it.
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08:29
Because, when people
from diverse backgrounds come together,
from diverse backgrounds come together,
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因为，当不同背景的人走到一起时，
当我们以正确的方式做事情时，
08:33
when we build things in the right way,
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就有无限的可能。
08:35
the possibilities are limitless.
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08:38
And that's what I want to end
by talking to you about.
by talking to you about.
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这就是我最后想和你们讨论的。
减少种族歧视的机器人，
减少夺走我们工作的机器——
减少夺走我们工作的机器——
08:42
Less racist robots, less machines
that are going to take our jobs --
that are going to take our jobs --
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更多专注于技术究竟能实现什么。
08:46
and more about what technology
can actually achieve.
can actually achieve.
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08:50
So, yes, some of the energy
in the world of AI,
in the world of AI,
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是的，人工智能世界中，
科技世界中的一些能量
08:53
in the world of technology
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08:55
is going to be about
what ads you see on your stream.
what ads you see on your stream.
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是关于你在流媒体中看到的广告。
但更多是朝着让世界更美好的方向前进。
08:59
But a lot of it is going towards
making the world so much better.
making the world so much better.
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09:05
Think about a pregnant woman
in the Democratic Republic of Congo,
in the Democratic Republic of Congo,
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想想刚果民主共和国的一位孕妇，
需要走17小时才能
到最近的农村产前诊所
到最近的农村产前诊所
09:09
who has to walk 17 hours
to her nearest rural prenatal clinic
to her nearest rural prenatal clinic
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进行产检。
09:13
to get a checkup.
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如果她在手机上
就能得到诊断会怎样呢？
就能得到诊断会怎样呢？
09:15
What if she could get diagnosis
on her phone, instead?
on her phone, instead?
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09:19
Or think about what AI could do
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或者想象一下人工智能
能为1/3面临家庭暴力的
09:21
for those one in three women
in South Africa
in South Africa
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南非女性做什么。
09:24
who face domestic violence.
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09:27
If it wasn't safe to talk out loud,
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如果大声说出来不安全的话，
她们可以通过一个
人工智能服务来报警，
人工智能服务来报警，
09:29
they could get an AI service
to raise alarm,
to raise alarm,
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获得财务和法律咨询。
09:32
get financial and legal advice.
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09:35
These are all real examples of projects
that people, including myself,
that people, including myself,
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这些都是包括我在内，
正在使用人工智能的人
正在使用人工智能的人
所做的项目中的真实案例。
09:41
are working on right now, using AI.
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09:45
So, I'm sure in the next couple of days
there will be yet another news story
there will be yet another news story
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我确信在未来的几十天里面，
会有另一个新闻故事，
会有另一个新闻故事，
09:49
about the existential risk,
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告诉你们，
机器人会接管你们的工作。
09:51
robots taking over
and coming for your jobs.
and coming for your jobs.
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（笑声）
09:54
(Laughter)
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当这样的事情发生时，
09:55
And when something like that happens,
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我知道我会收到
同样对未来表示担忧的信息。
同样对未来表示担忧的信息。
09:57
I know I'll get the same messages
worrying about the future.
worrying about the future.
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但我对这个技术极为乐观。
10:01
But I feel incredibly positive
about this technology.
about this technology.
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10:07
This is our chance to remake the world
into a much more equal place.
into a much more equal place.
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这是我们重新让世界
变得更平等的机会。
变得更平等的机会。
10:14
But to do that, we need to build it
the right way from the get go.
the right way from the get go.
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但要做到这一点，我们需要
在一开始就以正确的方式构建它。
在一开始就以正确的方式构建它。
10:19
We need people of different genders,
races, sexualities and backgrounds.
races, sexualities and backgrounds.
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我们需要不同性别，种族，
性取向和背景的人。
性取向和背景的人。
10:26
We need women to be the makers
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我们需要女性成为创造者，
而不仅仅是听从创造者命令的机器。
10:28
and not just the machines
who do the makers' bidding.
who do the makers' bidding.
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10:33
We need to think very carefully
what we teach machines,
what we teach machines,
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我们需要仔细思考
我们教给机器的东西，
我们教给机器的东西，
我们给它们什么数据，
10:37
what data we give them,
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这样它们就不会
只是重复我们过去的错误。
只是重复我们过去的错误。
10:39
so they don't just repeat
our own past mistakes.
our own past mistakes.
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10:44
So I hope I leave you
thinking about two things.
thinking about two things.
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所以我希望我留给你们两个思考。
10:48
First, I hope you leave
thinking about bias today.
thinking about bias today.
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首先，我希望你们思考
当今社会中的的偏见。
当今社会中的的偏见。
下次当你滚动刷到
10:53
And that the next time
you scroll past an advert
you scroll past an advert
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认为你对生育诊所
10:56
that assumes you are interested
in fertility clinics
in fertility clinics
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或者网上投注站有兴趣的广告时，
10:59
or online betting websites,
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这会让你回想起
11:02
that you think and remember
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同样的技术也在假定
黑人会重复犯罪。
黑人会重复犯罪。
11:04
that the same technology is assuming
that a black man will reoffend.
that a black man will reoffend.
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11:09
Or that a woman is more likely
to be a personal assistant than a CEO.
to be a personal assistant than a CEO.
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或者女性更可能成为
个人助理而非CEO。
个人助理而非CEO。
11:14
And I hope that reminds you
that we need to do something about it.
that we need to do something about it.
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我希望那会提醒你，
我们需要对此有所行动。
我们需要对此有所行动。
11:20
And second,
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第二，
我希望你们考虑一下这个事实，
11:22
I hope you think about the fact
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你不需要以特定的方式去看，
11:24
that you don't need to look a certain way
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也不需要有一定的工程或技术背景
11:26
or have a certain background
in engineering or technology
in engineering or technology
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去创建人工智能，
11:30
to create AI,
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人工智能将成为我们未来的
一股非凡力量。
一股非凡力量。
11:31
which is going to be
a phenomenal force for our future.
a phenomenal force for our future.
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11:36
You don't need to look
like a Mark Zuckerberg,
like a Mark Zuckerberg,
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你不需要看起来像马克·扎克伯格，
你可以看起来像我。
11:38
you can look like me.
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11:41
And it is up to all of us in this room
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我们这个房间里的所有人都有责任
去说服政府和公司
11:44
to convince the governments
and the corporations
and the corporations
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为每个人创建人工智能技术，
11:46
to build AI technology for everyone,
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包括边缘的情况。
11:49
including the edge cases.
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让我们所有人都能
11:52
And for us all to get education
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在未来接受有关这项非凡技术的教育。
11:54
about this phenomenal
technology in the future.
technology in the future.
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11:58
Because if we do that,
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因为如果我们那样做了，
才刚刚打开了人工智能世界的大门。
12:00
then we've only just scratched the surface
of what we can achieve with AI.
of what we can achieve with AI.
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谢谢。
12:05
Thank you.
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（鼓掌）
12:06
(Applause)
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ABOUT THE SPEAKER
Kriti Sharma - AI technologistKriti Sharma creates AI technology to help address some of the toughest social challenges of our time -- from domestic violence to sexual health and inequality.
Why you should listen
Kriti Sharma is the Founder of AI for Good, an organization focused on building scalable technology solutions for social good. In 2018, she also launched rAInbow, a digital companion for women facing domestic violence in South Africa. This service reached nearly 200,000 conversations within the first 100 days, breaking down the stigma of gender-based violence. In 2019, she collaborated with the Population Foundation of India to launch Dr. Sneha, an AI-powered digital character to engage with young people about sexual health, an issue that is still considered a taboo in India.
Sharma was recently named in the Forbes "30 Under 30" list for advancements in AI. She was appointed a United Nations Young Leader in 2018 and is an advisor to both the United Nations Technology Innovation Labs and to the UK Government’s Centre for Data Ethics and Innovation.
Kriti Sharma | Speaker | TED.com