TEDxWarwick
Kriti Sharma: How to keep human bias out of AI
克里蒂沙馬: 如何防止人工智慧學到人類偏見
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2,050,106 views
人工智慧演算法隨時都在做和你有關的決策——比如你的汽車保險保費應該要多高，或你是否能參加工作面試。但，如果這些機器的系統中內建有人類偏見的程式碼，會如何？科技專家克里蒂沙馬在這場演說中帶大家探究，技術界缺乏多樣性的現象如何偷偷進入了人工智慧，並提供三種方式，來協助我們開始開發更有倫理的演算法。
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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今天，有多少與你有關的決策，
00:16
or this week or this year,
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或這週，或今年，
00:19
by artificial intelligence?
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是由人工智慧所做的？
00:22
I build AI for a living
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我的工作是建造人工智慧，
00:24
so, full disclosure, I'm kind of a nerd.
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所以，不隱瞞大家，我是個怪胎。
00:27
And because I'm kind of a nerd,
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因為我是個怪胎，
00:30
wherever some new news story comes out
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每當有新的新聞報導出來，
00:32
about artificial intelligence
stealing all our jobs,
stealing all our jobs,
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內容有談到人工智慧
偷走我們所有的工作，
偷走我們所有的工作，
00:35
or robots getting citizenship
of an actual country,
of an actual country,
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或是機器人取得
實際國家的公民權，
實際國家的公民權，
00:40
I'm the person my friends
and followers message
and followers message
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我的朋友和追隨者
就會發訊息給我，
就會發訊息給我，
00:43
freaking out about the future.
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表示對於未來的擔憂。
00:45
We see this everywhere.
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這種狀況處處可見。
00:47
This media panic that
our robot overlords are taking over.
our robot overlords are taking over.
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這種認為機器人統治者
會接管世界的媒體恐慌。
會接管世界的媒體恐慌。
00:52
We could blame Hollywood for that.
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我們可以怪罪於好萊塢。
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，在現實中，我們不該
把焦點放在那個問題上。
把焦點放在那個問題上。
01:01
There is a more pressing danger,
a bigger risk with AI,
a bigger risk with AI,
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還有更迫切的危機，
人工智慧有個更大的風險，
人工智慧有個更大的風險，
01:04
that we need to fix first.
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我們應該要先解決它。
01:07
So we are back to this question:
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所以，回到這個問題：
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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今天，人工智慧做了
多少關於你的決策？
多少關於你的決策？
01:15
And how many of these
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這些決策中，有多少
01:17
were based on your gender,
your race or your background?
your race or your background?
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是根據你的性別、你的種族，
或你的背景所做出來的？
或你的背景所做出來的？
01:24
Algorithms are being used all the time
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演算法常常被拿來使用，
01:27
to make decisions about who we are
and what we want.
and what we want.
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做出關於我們是什麼人、
我們想要什麼的相關決策。
我們想要什麼的相關決策。
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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這間房間中有一些女性
知道我在說什麼，
知道我在說什麼，
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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如果你曾經坐在電腦前
看 YouTube 時，
看 YouTube 時，
被迫看完驗孕測試的廣告，
且發生過約一千次的話。
且發生過約一千次的話。
01:39
like 1,000 times.
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01:41
Or you've scrolled past adverts
of fertility clinics
of fertility clinics
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或者，你曾經在滑手機
看臉書動態時報時
看臉書動態時報時
01:44
on your Facebook feed.
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一直看到不孕症診所的廣告。
01:47
Or in my case, Indian marriage bureaus.
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或者，我的例子則是看到
印度婚姻介紹所的廣告。
印度婚姻介紹所的廣告。
01:50
(Laughter)
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（笑聲）
01:51
But AI isn't just being used
to make decisions
to make decisions
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但，人工智慧不只是被用來判定
01:54
about what products we want to buy
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我們想要買什麼產品，
01:56
or which show we want to binge watch next.
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或是我們接下來想要看追哪齣劇。
02:01
I wonder how you'd feel about someone
who thought things like this:
who thought things like this:
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我很好奇，對於這樣想的人，
你們有何感覺：
你們有何感覺：
02:06
"A black or Latino person
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「黑人或拉丁裔的人
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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準時還清貸款的可能性
沒有白人高。」
沒有白人高。」
02:13
"A person called John
makes a better programmer
makes a better programmer
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「名字叫做約翰的人，
和名叫瑪莉的人相比，
和名叫瑪莉的人相比，
02:16
than a person called Mary."
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會是比較好的程式設計師。」
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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「比起白人，黑人比較
有可能會再次犯罪。」
有可能會再次犯罪。」
02:26
You're probably thinking,
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你們可能在想：
02:28
"Wow, that sounds like a pretty sexist,
racist person," right?
racist person," right?
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「哇，那聽起來是性別主義
和種族主義的人會說的話」對吧？
和種族主義的人會說的話」對吧？
02:33
These are some real decisions
that AI has made very recently,
that AI has made very recently,
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上述這些是人工智慧
近期所做出的一些決策，
近期所做出的一些決策，
02:37
based on the biases
it has learned from us,
it has learned from us,
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決策依據是它向我們學來的偏見，
02:40
from the humans.
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向人類學來的。
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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人工智慧被用來協助判斷
你是否能參加工作面試；
你是否能參加工作面試；
02:48
how much you pay for your car insurance;
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你的汽車保險保費是多少錢；
02:51
how good your credit score is;
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你的信用評等有多好；
02:52
and even what rating you get
in your annual performance review.
in your annual performance review.
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甚至你在年度考績中
得到的評級是多少。
得到的評級是多少。
02:57
But these decisions
are all being filtered through
are all being filtered through
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但這些決策都會先被過濾過，
03:00
its assumptions about our identity,
our race, our gender, our age.
our race, our gender, our age.
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過濾依據就是關於我們的身分、
種族、性別、年齡等的假設。
種族、性別、年齡等的假設。
03:08
How is that happening?
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為什麼會發生這種事？
03:10
Now, imagine an AI is helping
a hiring manager
a hiring manager
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想像一下，人工智慧在協助
一位有人才需求的經理
一位有人才需求的經理
03:14
find the next tech leader in the company.
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尋找該公司的下一位技術主管。
03:16
So far, the manager
has been hiring mostly men.
has been hiring mostly men.
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目前，這位經理僱用的人
大部分都是男性。
大部分都是男性。
03:20
So the AI learns men are more likely
to be programmers than women.
to be programmers than women.
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所以人工智慧學到的是，男性
比女性更有可能成為程式設計師。
比女性更有可能成為程式設計師。
03:25
And it's a very short leap from there to:
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很容易就會從
這個現象直接下結論：
這個現象直接下結論：
03:28
men make better programmers than women.
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男性程式設計師比女性好。
03:31
We have reinforced
our own bias into the AI.
our own bias into the AI.
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我們把我們自己的偏見
灌輸給人工智慧。
灌輸給人工智慧。
03:35
And now, it's screening out
female candidates.
female candidates.
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現在，它就會把
女性候選人給篩掉。
女性候選人給篩掉。
03:40
Hang on, if a human
hiring manager did that,
hiring manager did that,
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等等，如果人類的經理這樣做，
03:43
we'd be outraged, we wouldn't allow it.
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我們會很火大，
我們不會容忍這種事。
我們不會容忍這種事。
03:46
This kind of gender
discrimination is not OK.
discrimination is not OK.
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這種性別歧視是不對的。
03:49
And yet somehow,
AI has become above the law,
AI has become above the law,
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但，人工智慧卻以
某種方式超越了法律，
某種方式超越了法律，
03:54
because a machine made the decision.
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因為那個決策是機器做出來的。
03:57
That's not it.
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不只如此。
03:59
We are also reinforcing our bias
in how we interact with AI.
in how we interact with AI.
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我們和人工智慧的互動，
也加強了我們自己的偏見。
也加強了我們自己的偏見。
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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你們有多常使用語音助手，比如
Siri、Alexa，或甚至 Cortana？
Siri、Alexa，或甚至 Cortana？
04:10
They all have two things in common:
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它們全都有兩項共通點：
04:13
one, they can never get my name right,
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第一，它們總是把我的名字弄錯，
04:16
and second, they are all female.
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第二，它們都是女性。
04:20
They are designed to be
our obedient servants,
our obedient servants,
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它們被設計為順從我們的僕人，
04:23
turning your lights on and off,
ordering your shopping.
ordering your shopping.
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幫你開燈、關燈，幫你下單購物。
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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也會有男性的人工智慧，
但通常它們的功能比較強，
但通常它們的功能比較強，
04:30
like IBM Watson,
making business decisions,
making business decisions,
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比如 IBM 的 Watson，
做的是商業決策，
做的是商業決策，
04:33
Salesforce Einstein
or ROSS, the robot lawyer.
or ROSS, the robot lawyer.
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又如 Salesforce Einstein，
或是機器律師 ROSS 。
或是機器律師 ROSS 。
04:38
So poor robots, even they suffer
from sexism in the workplace.
from sexism in the workplace.
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可憐的機器人，連它們也會
遇到工作場所的性別主義。
遇到工作場所的性別主義。
04:42
(Laughter)
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（笑聲）
04:44
Think about how these two things combine
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想想看，當這兩點結合起來時，
04:47
and affect a kid growing up
in today's world around AI.
in today's world around AI.
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會如何影響現今世界中與人工智慧
一同生活的孩子成長。
一同生活的孩子成長。
04:52
So they're doing some research
for a school project
for a school project
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所以，他們為了一項學校
專案計畫做了些研究，
專案計畫做了些研究，
04:55
and they Google images of CEO.
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他們用 Google 搜尋了
執行長的形象。
執行長的形象。
04:58
The algorithm shows them
results of mostly men.
results of mostly men.
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演算法呈現給他們看的搜尋結果，
大部分都是男性。
大部分都是男性。
05:01
And now, they Google personal assistant.
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他們又搜尋了個人助理。
05:04
As you can guess,
it shows them mostly females.
it shows them mostly females.
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你們可以猜到，呈現出來的
搜尋結果大部分是女性。
搜尋結果大部分是女性。
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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接著，他們想要播放音樂，
也許再點一些食物來吃，
也許再點一些食物來吃，
05:11
and now, they are barking orders
at an obedient female voice assistant.
at an obedient female voice assistant.
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現在，他們便大聲喊出命令，
要順從的女性語音助手去做。
05:19
Some of our brightest minds
are creating this technology today.
are creating this technology today.
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一些最聰明的天才們
創造出現今的這種技術。
創造出現今的這種技術。
05:24
Technology that they could have created
in any way they wanted.
in any way they wanted.
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他們可以依他們想要的
任何方式來創造這種技術。
任何方式來創造這種技術。
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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但，他們選擇採用五〇年代
《廣告狂人》中的秘書風格。
《廣告狂人》中的秘書風格。
05:34
Yay!
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好呀！
05:36
But OK, don't worry,
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但，好，別擔心，
05:38
this is not going to end
with me telling you
with me telling you
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演說的結尾不會是我告訴各位
05:40
that we are all heading towards
sexist, racist machines running the world.
sexist, racist machines running the world.
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我們正在邁向一個由性別主義、
種族主義的機器所統治的世界。
種族主義的機器所統治的世界。
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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關於人工智慧的好消息是，
它完全在我們的掌控當中。
它完全在我們的掌控當中。
05:51
We get to teach the right values,
the right ethics to AI.
the right ethics to AI.
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我們可以教導人工智慧
正確的價值觀、正確的倫理。
正確的價值觀、正確的倫理。
05:56
So there are three things we can do.
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有三件事是我們可以做的。
05:58
One, we can be aware of our own biases
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第一，我們可以意識到
我們自己有偏見存在，
我們自己有偏見存在，
06:01
and the bias in machines around us.
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以及我們身邊的機器也有偏見。
06:04
Two, we can make sure that diverse teams
are building this technology.
are building this technology.
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第二，我們可以確保這項技術
是由多樣化的團隊來建造。
是由多樣化的團隊來建造。
06:09
And three, we have to give it
diverse experiences to learn from.
diverse experiences to learn from.
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第三，我們要提供多樣化的經驗，
讓這項技術從中學習。
讓這項技術從中學習。
06:14
I can talk about the first two
from personal experience.
from personal experience.
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我可以從個人經歷來談前兩點。
06:18
When you work in technology
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當你在科技業工作，
06:19
and you don't look like
a Mark Zuckerberg or Elon Musk,
a Mark Zuckerberg or Elon Musk,
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且你看起來並不像是
馬克祖克柏或伊隆馬斯克，
馬克祖克柏或伊隆馬斯克，
06:23
your life is a little bit difficult,
your ability gets questioned.
your ability gets questioned.
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你的生活就會有一點辛苦，
你的能力會被質疑。
你的能力會被質疑。
06:27
Here's just one example.
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這只是一個例子。
06:29
Like most developers,
I often join online tech forums
I often join online tech forums
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和大部分的開發者一樣，
我通常會加入線上技術討論區，
我通常會加入線上技術討論區，
06:33
and share my knowledge to help others.
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分享我的知識來協助他人。
06:36
And I've found,
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而我發現，
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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當我用自己登入，放我自己的
照片，用我自己的名字，
照片，用我自己的名字，
06:41
I tend to get questions
or comments like this:
or comments like this:
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我常常會得到這樣的問題或意見：
06:46
"What makes you think
you're qualified to talk about AI?"
you're qualified to talk about AI?"
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「你怎麼會認為
你有資格談論人工智慧？」
你有資格談論人工智慧？」
06:50
"What makes you think
you know about machine learning?"
you know about machine learning?"
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「你怎麼會認為
你了解機器學習？」
你了解機器學習？」
06:53
So, as you do, I made a new profile,
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所以，跟大家一樣，
我會做個新的個人檔案，
我會做個新的個人檔案，
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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這次，我不放自己的照片，
我選的照片是一隻背著
噴氣飛行器的貓。
噴氣飛行器的貓。
07:02
And I chose a name
that did not reveal my gender.
that did not reveal my gender.
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我選用的名字看不出性別。
07:05
You can probably guess
where this is going, right?
where this is going, right?
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你們應該猜得出後續發展，對吧？
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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所以，這次，那些高人一等的人
完全沒有對我的能力提出意見，
07:15
and I was able to actually
get some work done.
get some work done.
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我還真的能完成一些事。
07:19
And it sucks, guys.
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各位，這真的很鳥。
07:21
I've been building robots since I was 15,
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我從十五歲時就在建造機器人了，
07:23
I have a few degrees in computer science,
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我有幾個資訊科學的學位，
07:26
and yet, I had to hide my gender
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但，我還是得隱瞞我的性別，
07:28
in order for my work
to be taken seriously.
to be taken seriously.
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我所做的事才會被認真看待。
07:31
So, what's going on here?
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這是怎麼回事？
07:33
Are men just better
at technology than women?
at technology than women?
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在科技上，男人就是
比女人厲害嗎？
比女人厲害嗎？
07:37
Another study found
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另一項研究發現，
07:39
that when women coders on one platform
hid their gender, like myself,
hid their gender, like myself,
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在平台上，當女性編碼者
像我這樣隱瞞自己的性別時，
像我這樣隱瞞自己的性別時，
07:44
their code was accepted
four percent more than men.
four percent more than men.
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她們的程式碼被接受的
比率比男性高 4%。
比率比男性高 4%。
07:48
So this is not about the talent.
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重點並不是才華。
07:51
This is about an elitism in AI
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重點是人工智慧領域的精英主義，
07:54
that says a programmer
needs to look like a certain person.
needs to look like a certain person.
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它說，程式設計師必須要
看起來像是某種人。
看起來像是某種人。
07:59
What we really need to do
to make AI better
to make AI better
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若想要讓人工智慧更好，
我們需要做的事情
我們需要做的事情
08:02
is bring people
from all kinds of backgrounds.
from all kinds of backgrounds.
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是集合各種背景的人。
08:06
We need people who can
write and tell stories
write and tell stories
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我們需要能夠寫故事、說故事的人
08:09
to help us create personalities of AI.
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來協助我們創造出
人工智慧的人格。
人工智慧的人格。
08:12
We need people who can solve problems.
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我們需要能夠解決問題的人。
08:15
We need people
who face different challenges
who face different challenges
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我們需要能夠面對不同挑戰的人，
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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我們需要能夠告訴我們
真正需要修正的問題是什麼，
真正需要修正的問題是什麼，
08:24
and help us find ways
that technology can actually fix it.
that technology can actually fix it.
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且協助我們想辦法
用科技來修正它的人。
用科技來修正它的人。
08:29
Because, when people
from diverse backgrounds come together,
from diverse backgrounds come together,
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因為，當來自多樣化
背景的人集結在一起，
背景的人集結在一起，
08:33
when we build things in the right way,
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當我們用對的方式建造新東西時，
08:35
the possibilities are limitless.
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就會有無限的可能性。
08:38
And that's what I want to end
by talking to you about.
by talking to you about.
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我希望用這一點
來結束今天的演說。
來結束今天的演說。
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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少談種族主義的機器人、
少談機器會搶走我們的工作——
少談機器會搶走我們的工作——
08:46
and more about what technology
can actually achieve.
can actually achieve.
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多談科技能夠達成什麼。
08:50
So, yes, some of the energy
in the world of AI,
in the world of AI,
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所以，是的，在人工智慧
世界中的某些能量，
世界中的某些能量，
08:53
in the world of technology
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在科技世界中的某些能量，
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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會被用來決定放什麼廣告
到你的串流中。
到你的串流中。
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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但有很大一部分的目的
會是要讓世界變得更好。
會是要讓世界變得更好。
09:05
Think about a pregnant woman
in the Democratic Republic of Congo,
in the Democratic Republic of Congo,
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想想看，在剛果民主
共和國的懷孕女子，
共和國的懷孕女子，
09:09
who has to walk 17 hours
to her nearest rural prenatal clinic
to her nearest rural prenatal clinic
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她得要走十七小時的路，
才能到達最近的鄉村婦產科診所，
才能到達最近的鄉村婦產科診所，
09:13
to get a checkup.
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去做一次檢查。
09:15
What if she could get diagnosis
on her phone, instead?
on her phone, instead?
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如果她能夠改用她的手機
來取得診斷呢？
來取得診斷呢？
09:19
Or think about what AI could do
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或者，想想人工智慧能做什麼，
09:21
for those one in three women
in South Africa
in South Africa
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來協助解決南非有三分之一女性
09:24
who face domestic violence.
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要面對家暴的問題。
09:27
If it wasn't safe to talk out loud,
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如果大聲談論並不安全，
09:29
they could get an AI service
to raise alarm,
to raise alarm,
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她們可以透過人工智慧服務來求援，
09:32
get financial and legal advice.
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取得財務和法律建議。
09:35
These are all real examples of projects
that people, including myself,
that people, including myself,
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這些例子都是目前
有人在利用人工智慧
有人在利用人工智慧
09:41
are working on right now, using AI.
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進行的專案計畫，包括我在內。
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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我相信，在接下來幾天，
還會有另一則新聞報導，
還會有另一則新聞報導，
09:49
about the existential risk,
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談及生存危機、
09:51
robots taking over
and coming for your jobs.
and coming for your jobs.
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機器人即將來搶走你的工作。
09:54
(Laughter)
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（笑聲）
09:55
And when something like that happens,
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當發生這樣的狀況時，
09:57
I know I'll get the same messages
worrying about the future.
worrying about the future.
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我知道我又會收到
關於擔心未來的訊息。
關於擔心未來的訊息。
10:01
But I feel incredibly positive
about this technology.
about this technology.
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但我對這項技術的感受
是非常正面的。
是非常正面的。
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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這是一個機會，
我們可以把世界重建，
我們可以把世界重建，
成為更平等的地方。
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，若想做到這個目標，
打從一開始就要用對方式。
打從一開始就要用對方式。
10:19
We need people of different genders,
races, sexualities and backgrounds.
races, sexualities and backgrounds.
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我們需要不同性別、
種族、性向，和背景的人。
種族、性向，和背景的人。
10:26
We need women to be the makers
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我們需要女性來當創造者，
10:28
and not just the machines
who do the makers' bidding.
who do the makers' bidding.
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不只是會照著創造者的
命令做事的機器。
命令做事的機器。
10:33
We need to think very carefully
what we teach machines,
what we teach machines,
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我們得要非常小心地思考
我們要教導機器什麼，
我們要教導機器什麼，
10:37
what data we give them,
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要給它們什麼資料，
10:39
so they don't just repeat
our own past mistakes.
our own past mistakes.
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以免它們重蹈我們過去的覆轍。
10:44
So I hope I leave you
thinking about two things.
thinking about two things.
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我希望留下兩件事讓各位思考。
10:48
First, I hope you leave
thinking about bias today.
thinking about bias today.
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第一，我希望大家離開這裡之後，
能想想現今的偏見。
能想想現今的偏見。
10:53
And that the next time
you scroll past an advert
you scroll past an advert
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下次當你滑手機看到廣告時，
10:56
that assumes you are interested
in fertility clinics
in fertility clinics
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且廣告內容是假設
你想了解不孕症診所
你想了解不孕症診所
10:59
or online betting websites,
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或線上賭博網站，
11:02
that you think and remember
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你就要思考一下並想起來，
11:04
that the same technology is assuming
that a black man will reoffend.
that a black man will reoffend.
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這項技術也同樣會假設
黑人會再犯罪。
黑人會再犯罪。
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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或者女性比較有可能
成為個人助理而非執行長。
成為個人助理而非執行長。
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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我希望那能夠提醒各位，
我們得要採取行動。
我們得要採取行動。
11:20
And second,
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第二，
11:22
I hope you think about the fact
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我希望大家能想想，
11:24
that you don't need to look a certain way
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你並不需要有某種外表
11:26
or have a certain background
in engineering or technology
in engineering or technology
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或某種工程或科技背景，
11:30
to create AI,
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才能創造人工智慧，
11:31
which is going to be
a phenomenal force for our future.
a phenomenal force for our future.
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它將會是我們未來的
一股驚人力量。
一股驚人力量。
11:36
You don't need to look
like a Mark Zuckerberg,
like a Mark Zuckerberg,
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你不需要看起來像馬克祖克柏，
11:38
you can look like me.
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你可以看起來像我。
11:41
And it is up to all of us in this room
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要靠我們這間房間的所有人，
11:44
to convince the governments
and the corporations
and the corporations
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來說服政府和企業，
11:46
to build AI technology for everyone,
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為每個人建造人工智慧技術，
11:49
including the edge cases.
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包括邊緣的個案。
11:52
And for us all to get education
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而我們所有人將來都應該要
接受關於這項重大技術的教育。
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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因為，如果能這麼做，
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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那麼我們還能夠用人工智慧
做出更多了不起的事。
做出更多了不起的事。
12:05
Thank you.
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謝謝。
12:06
(Applause)
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（掌聲）
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