The Way We Work
Priyanka Jain: How to make applying for jobs less painful
朴雅卡· 耆那: 怎样让工作申请少一些痛苦
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找工作时通常先要把你的简历提交给数不清的雇主，然后大多数永远不会有回应。但是越来越多的公司正在使用前沿的技术识别候选人。如果AI成为招聘的未来，它对你将意味着什么？技术专家朴雅卡· 耆那向我们展示了这种新的招聘场景。
Priyanka Jain - Technologist
Priyanka Jain heads up product for pymetrics, an NYC-based startup that uses neuroscience and AI to make hiring more diverse and effective. Full bio
Priyanka Jain heads up product for pymetrics, an NYC-based startup that uses neuroscience and AI to make hiring more diverse and effective. Full bio
Double-click the English transcript below to play the video.
00:00
Applying for jobs online
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在线申请工作
是我们这个时代最糟糕的
数字化体验之一。
数字化体验之一。
00:01
is one of the worst
digital experiences of our time.
digital experiences of our time.
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面对面交谈也没好到哪儿去。
00:04
And applying for jobs in person
really isn't much better.
really isn't much better.
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【我们的工作方式】
00:07
[The Way We Work]
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00:11
Hiring as we know it
is broken on many fronts.
is broken on many fronts.
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众所周知，招聘方式
在很多方面一团糟。
在很多方面一团糟。
00:14
It's a terrible experience for people.
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对人们来说这是一个糟糕的经历。
在过去一年
00:15
About 75 percent of people
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使用多种方式申请工作时的
群体中，大约有75%的人
群体中，大约有75%的人
00:17
who applied to jobs
using various methods in the past year
using various methods in the past year
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说他们从未收到雇主的任何反馈。
00:20
said they never heard anything back
from the employer.
from the employer.
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对公司来说，这不是一件好事情。
00:23
And at the company level
it's not much better.
it's not much better.
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在开始工作的不到一年时间里，
00:25
46 percent of people get fired or quit
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46%的人被解雇或者主动离职。
00:28
within the first year
of starting their jobs.
of starting their jobs.
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这一点很令人震惊。
00:30
It's pretty mind-blowing.
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这种现象对经济也产生了负面影响。
00:31
It's also bad for the economy.
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在历史上第一次，
00:33
For the first time in history,
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招聘岗位超过了无业人员的人数，
00:34
we have more open jobs
than we have unemployed people,
than we have unemployed people,
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对我而言，这意味着出问题了。
00:37
and to me that screams
that we have a problem.
that we have a problem.
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我认为这一切的关键在于一张纸：
简历。
简历。
00:39
I believe that at the crux of all of this
is a single piece of paper: the résumé.
is a single piece of paper: the résumé.
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毫无疑问，简历中包含着一些
有用的信息：
有用的信息：
00:43
A résumé definitely has
some useful pieces in it:
some useful pieces in it:
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人们扮演过哪些角色，
有哪些计算机技能，
有哪些计算机技能，
00:46
what roles people have had,
computer skills,
computer skills,
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精通什么语言，
00:48
what languages they speak,
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但并未提及他们有哪方面的潜力，
00:49
but what it misses is
what they have the potential to do
what they have the potential to do
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这些事情他们在过去可能没机会去做。
00:52
that they might not have had
the opportunity to do in the past.
the opportunity to do in the past.
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在变化如此迅速的经济环境中，
在线发布的工作机会
在线发布的工作机会
00:55
And with such a quickly changing economy
where jobs are coming online
where jobs are coming online
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可能要求的都是没人掌握的技术，
00:58
that might require skills that nobody has,
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如果我们只看一个人过去做了什么，
01:01
if we only look at what someone
has done in the past,
has done in the past,
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就不能把这个人和
未来的工作匹配起来。
未来的工作匹配起来。
01:03
we're not going to be able
to match people to the jobs of the future.
to match people to the jobs of the future.
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所以我认为这是技术真正有用的地方。
01:07
So this is where I think technology
can be really helpful.
can be really helpful.
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您可能已经看到了算法如何很好的
01:09
You've probably seen
that algorithms have gotten pretty good
that algorithms have gotten pretty good
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把人和事物匹配起来，
01:12
at matching people to things,
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但是如果我们把同样的技术用于
01:14
but what if we could use
that same technology
that same technology
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真正帮助找到那些为
我们量身打造的工作昵？
我们量身打造的工作昵？
01:16
to actually help us find jobs
that we're really well-suited for?
that we're really well-suited for?
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我知道你在想什么。
01:19
But I know what you're thinking.
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让算法为你挑拣下一份工作
听起来有点离谱，
听起来有点离谱，
01:21
Algorithms picking your next job
sounds a little bit scary,
sounds a little bit scary,
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但有个东西已经被证明
01:24
but there is one thing that has been shown
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能够成功预测某人
是否能胜任未来的工作，
是否能胜任未来的工作，
01:26
to be really predictive
of someone's future success in a job,
of someone's future success in a job,
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这就是所谓的多评估测试。
01:29
and that's what's called
a multimeasure test.
a multimeasure test.
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多评估测试并不是什么新概念，
01:31
Multimeasure tests
really aren't anything new,
really aren't anything new,
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但是它们曾经价格不菲，
01:33
but they used to be really expensive
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并且需要一个博士坐在你对面，
01:35
and required a PhD sitting across from you
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回答一堆问题并且整理成报告。
01:37
and answering lots of questions
and writing reports.
and writing reports.
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多评估测试是一种用来
01:39
Multimeasure tests are a way
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理解某人内在特质的方法——
01:41
to understand someone's inherent traits --
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你的记忆力，你的专注力。
01:43
your memory, your attentiveness.
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01:46
What if we could take multimeasure tests
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如果我们能够做多评估测试，
让公众都可以参与，
01:48
and make them scalable and accessible,
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并且把相关数据提供给雇主，
比如某个人的某些特质
比如某个人的某些特质
01:51
and provide data to employers
about really what the traits are
about really what the traits are
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使其真的很适合这个工作，会怎样？
01:54
of someone who can make
them a good fit for a job?
them a good fit for a job?
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这些听起来很抽象。
01:57
This all sounds abstract.
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让我们一起试试其中一个游戏。
01:58
Let's try one of the games together.
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你将要看到一个闪烁的圆，
02:00
You're about to see a flashing circle,
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你的任务就是当圆是红色时鼓掌，
02:02
and your job is going to be
to clap when the circle is red
to clap when the circle is red
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02:06
and do nothing when it's green.
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当圆是绿色时什么也不做。
【准备好了？】
02:07
[Ready?]
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【开始】
02:09
[Begin!]
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02:11
[Green circle]
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【绿色圆】
02:13
[Green circle]
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【绿色圆】
02:15
[Red circle]
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【红色圆】
02:17
[Green circle]
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【绿色圆】
02:19
[Red circle]
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【红色圆】
02:21
Maybe you're the type of person
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或许你是那种
在红色圆出现后毫秒内鼓掌的人。
02:23
who claps the millisecond
after a red circle appears.
after a red circle appears.
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或者你是另外一种人，
02:26
Or maybe you're the type of person
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那种需要多花点时间，
等到100%确认才行动的人。
等到100%确认才行动的人。
02:27
who takes just a little bit longer
to be 100 percent sure.
to be 100 percent sure.
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或者你在还不确定时
就为绿色圆鼓掌。
就为绿色圆鼓掌。
02:30
Or maybe you clap on green
even though you're not supposed to.
even though you're not supposed to.
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很酷的一点是这并不
像是个标准的测试，
像是个标准的测试，
02:33
The cool thing here is that
this isn't like a standardized test
this isn't like a standardized test
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那种决定能被雇佣与否的测试。
02:36
where some people are employable
and some people aren't.
and some people aren't.
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相反，这是个理解你的特性和
02:39
Instead it's about understanding
the fit between your characteristics
the fit between your characteristics
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适合你的工作之间的匹配度测试。
02:42
and what would make you
good a certain job.
good a certain job.
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我们发现如果你在红色时鼓掌晚，
而在绿色时从不鼓掌，
而在绿色时从不鼓掌，
02:44
We found that if you clap late on red
and you never clap on the green,
and you never clap on the green,
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你可能具备高度专注力，
能够很好的自我约束。
能够很好的自我约束。
02:48
you might be high in attentiveness
and high in restraint.
and high in restraint.
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在那个象限的人们
往往擅长学习和考试，
往往擅长学习和考试，
02:51
People in that quadrant tend to be
great students, great test-takers,
great students, great test-takers,
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精于项目管理和财会。
02:55
great at project management or accounting.
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如果你在红色时立刻鼓掌，
并且有时在绿色鼓掌，
并且有时在绿色鼓掌，
02:57
But if you clap immediately on red
and sometimes clap on green,
and sometimes clap on green,
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那意味着你可能易冲动
并且具备创造性，
并且具备创造性，
03:00
that might mean that
you're more impulsive and creative,
you're more impulsive and creative,
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我们发现顶级的商人
经常会表现出这些特质。
经常会表现出这些特质。
03:03
and we've found that top-performing
salespeople often embody these traits.
salespeople often embody these traits.
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我们在招聘中使用它的方式是
03:07
The way we actually use this in hiring
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我们让角色中表现出色的人参与
03:09
is we have top performers in a role
go through neuroscience exercises
go through neuroscience exercises
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类似的神经科学训练。
03:12
like this one.
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然后我们开发了一个算法
03:14
Then we develop an algorithm
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来理解是什么让这些
表现出众者脱颖而出。
表现出众者脱颖而出。
03:15
that understands what makes
those top performers unique.
those top performers unique.
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然后当人们申请工作的时候，
03:18
And then when people apply to the job,
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我们就会优先列出
最适合那项工作的候选人。
最适合那项工作的候选人。
03:20
we're able to surface the candidates
who might be best suited for that job.
who might be best suited for that job.
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你可能在思考其中存在的风险。
03:24
So you might be thinking
there's a danger in this.
there's a danger in this.
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当今的职场多样性仍有待提高，
03:26
The work world today
is not the most diverse
is not the most diverse
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如果我们基于当下的
出众员工构建算法，
出众员工构建算法，
03:28
and if we're building algorithms
based on current top performers,
based on current top performers,
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要怎样确保
03:32
how do we make sure
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我们不是在固守既有的偏见呢？
03:33
that we're not just perpetuating
the biases that already exist?
the biases that already exist?
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例如，如果我们基于顶尖表现的
CEO构建一个算法
CEO构建一个算法
03:36
For example, if we were building
an algorithm based on top performing CEOs
an algorithm based on top performing CEOs
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并且使用S&P500作为一个训练集，
03:40
and use the S&P 500 as a training set,
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你将会发现
03:43
you would actually find
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更可能雇佣一个叫约翰的
白人男子而非任何女性。
白人男子而非任何女性。
03:44
that you're more likely to hire
a white man named John than any woman.
a white man named John than any woman.
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这是目前谁正处在这个角色的现实。
03:48
And that's the reality
of who's in those roles right now.
of who's in those roles right now.
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但是技术实际上给出了
一个真正有趣的机会。
一个真正有趣的机会。
03:51
But technology actually poses
a really interesting opportunity.
a really interesting opportunity.
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我们可以创造一些比人类
任何时候都更平等
任何时候都更平等
03:54
We can create algorithms
that are more equitable
that are more equitable
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和更公正的算法。
03:57
and more fair than human beings
have ever been.
have ever been.
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每一个我们投入生产的
算法都会被预先进行测试
算法都会被预先进行测试
03:59
Every algorithm that we put
into production has been pretested
into production has been pretested
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以确保它不会偏爱任何性别
或者种族。
或者种族。
04:03
to ensure that it doesn't favor
any gender or ethnicity.
any gender or ethnicity.
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如果有任何人群正在被过度偏爱，
04:06
And if there's any population
that's being overfavored,
that's being overfavored,
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我们可以调整算法直到该现象消失。
04:08
we can actually alter the algorithm
until that's no longer true.
until that's no longer true.
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04:12
When we focus on the inherent
characteristics
characteristics
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当我们关注在那些让一个人
非常适合一个工作的内在特质时，
04:14
that can make somebody
a good fit for a job,
a good fit for a job,
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我们可以超越种族，阶级，
性别和老龄化主义——
性别和老龄化主义——
04:16
we can transcend racism,
classism, sexism, ageism --
classism, sexism, ageism --
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甚至是名校背景。
04:20
even good schoolism.
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我们最好的技术和算法不应该只用于
04:21
Our best technology and algorithms
shouldn't just be used
shouldn't just be used
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帮助寻找我们的下一个卖座电影
或者贾斯汀·比伯的新歌。
或者贾斯汀·比伯的新歌。
04:24
for helping us find our next movie binge
or new favorite Justin Bieber song.
or new favorite Justin Bieber song.
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想象一下如果我们能够利用技术的
力量，
力量，
04:28
Imagine if we could harness
the power of technology
the power of technology
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在更深层次上理解我们是谁，
并得到一个
并得到一个
04:31
to get real guidance
on what we should be doing
on what we should be doing
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我们应该做什么的真正指引会怎样。
04:33
based on who we are at a deeper level.
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ABOUT THE SPEAKER
Priyanka Jain - TechnologistPriyanka Jain heads up product for pymetrics, an NYC-based startup that uses neuroscience and AI to make hiring more diverse and effective.
Why you should listen
Passionate about using technology to create a fairer workplace and global economy, Priyanka Jain is a spokesperson for the United Nations Foundation's Girl Up Campaign, Chair of the Acumen Fund's Junior Council and on the Innovation Board for the XPrize Foundation. She received her B.S. from Stanford University, where she was President of Stanford Women in Business and one of 12 Mayfield Entrepreneurship Fellows. Her previous experience includes internships at IBM Watson, Shift Technologies, Canvas Ventures and the Institute for Learning and Brain Sciences. Outside of work, she loves playing tennis and eating anything covered in dark chocolate.
Priyanka Jain | Speaker | TED.com