The Way We Work
Priyanka Jain: How to make applying for jobs less painful
菩里楊卡 · 簡恩: 如何讓找工作不那麼痛苦
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人們找工作不外乎就是從丟一堆的履歷開始，但大部分卻是石沉大海。然而有越來越多公司採用高科技方法來找出合適的人選。如果人工智慧是未來求職市場的趨勢，對你而言它意味著什麼呢？科技工作者菩里楊卡 · 簡恩（Priyanka Jain）帶我們一窺這個求職新視野。
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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網路上求職
00:01
is one of the worst
digital experiences of our time.
digital experiences of our time.
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是現代最糟糕的一種數位體驗，
00:04
And applying for jobs in person
really isn't much better.
really isn't much better.
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但親自求職也好不了多少。
00:07
[The Way We Work]
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【我們的工作方式】
00:11
Hiring as we know it
is broken on many fronts.
is broken on many fronts.
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我們所知的招聘方式
在很多方面存在缺陷，
在很多方面存在缺陷，
00:14
It's a terrible experience for people.
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對很多人來說都是難受的體驗。
00:15
About 75 percent of people
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過去一年中，
00:17
who applied to jobs
using various methods in the past year
using various methods in the past year
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以不同方式找工作的求職者裡面
00:20
said they never heard anything back
from the employer.
from the employer.
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有 75% 的人表示從未得到雇主回覆。
00:23
And at the company level
it's not much better.
it's not much better.
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而對招聘的公司來說，
情況也沒好到哪裡。
情況也沒好到哪裡。
00:25
46 percent of people get fired or quit
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任職不到一年
就被解聘或辭職的人也高達 46%，
00:28
within the first year
of starting their jobs.
of starting their jobs.
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00:30
It's pretty mind-blowing.
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實在令人震驚，
00:31
It's also bad for the economy.
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也不利於經濟發展。
00:33
For the first time in history,
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第一次在歷史上出現了
00:34
we have more open jobs
than we have unemployed people,
than we have unemployed people,
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職位空缺多於失業人數的現象，
00:37
and to me that screams
that we have a problem.
that we have a problem.
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這是個令人不容小覷的問題。
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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我認為所有問題的關鍵在於
那一張紙——也就是履歷表。
那一張紙——也就是履歷表。
00:43
A résumé definitely has
some useful pieces in it:
some useful pieces in it:
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履歷表固然有不少有用訊息：
00:46
what roles people have had,
computer skills,
computer skills,
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例如求職者曾經擔任的職位、
他們的電腦技能，
他們的電腦技能，
00:48
what languages they speak,
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及他們會的語言。
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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但履歷表無法顯示求職者的潛能，
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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因為他們過去沒有機會
去擔任能展現長才的工作。
去擔任能展現長才的工作。
00:55
And with such a quickly changing economy
where jobs are coming online
where jobs are coming online
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隨着經濟急促轉型，
網上湧現大批職缺
網上湧現大批職缺
00:58
that might require skills that nobody has,
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需要一些無前例可循的技能。
01:01
if we only look at what someone
has done in the past,
has done in the past,
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如果我們單看求職者過去的成就，
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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則無法為未來的職位找到合適人才。
因此我認為科技在這方面能幫上很多忙。
01:07
So this is where I think technology
can be really helpful.
can be really helpful.
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大家或許見識過演算法能針對需求
01:09
You've probably seen
that algorithms have gotten pretty good
that algorithms have gotten pretty good
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為人們找到適合的東西。
01:12
at matching people to things,
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01:14
but what if we could use
that same technology
that same technology
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那麼是否我們可以將相同的技術
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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應用在尋找適合的職缺呢？
01:19
But I know what you're thinking.
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我知道大家在想什麼，
用演算法來媒合工作聽起來有點可怕，
01:21
Algorithms picking your next job
sounds a little bit scary,
sounds a little bit scary,
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但有一項技術能夠預測
01:24
but there is one thing that has been shown
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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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求職者在新工作上的成就，
那就是所謂的「多元測試」。
01:29
and that's what's called
a multimeasure test.
a multimeasure test.
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多元測試並不是什麼新玩意兒，
01:31
Multimeasure tests
really aren't anything new,
really aren't anything new,
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以前它的成本很高，
01:33
but they used to be really expensive
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需要一位博士坐在你面前，
01:35
and required a PhD sitting across from you
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回答一大堆問題、寫一堆報告。
01:37
and answering lots of questions
and writing reports.
and writing reports.
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01:39
Multimeasure tests are a way
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多元測試能了解
01:41
to understand someone's inherent traits --
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一個人與生俱有的特色，
01:43
your memory, your attentiveness.
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例如：你的記憶力、注意力。
01:46
What if we could take multimeasure tests
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如果我們可以運用多元測試，
01:48
and make them scalable and accessible,
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讓它可量身訂做、普及，
01:51
and provide data to employers
about really what the traits are
about really what the traits are
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並將這些數據提供給雇主，
以個人特質來篩選
以個人特質來篩選
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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真的適合這項工作的人選呢？
01:57
This all sounds abstract.
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這聽起來很抽象。
不如，我們來玩個小遊戲。
01:58
Let's try one of the games together.
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遊戲中你會看到一個圓圈閃過，
02:00
You're about to see a flashing circle,
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如果你看到紅色圓圈，
就要立刻拍手，
就要立刻拍手，
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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如果是綠的，就不要做任何動作。
02:07
[Ready?]
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［準備好了沒？］
02:09
[Begin!]
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［開始！］
［綠色圓圈］
02:11
[Green circle]
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02:13
[Green circle]
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［綠色圓圈］
02:15
[Red circle]
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［紅色圓圈］
02:17
[Green circle]
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［綠色圓圈］
［紅色圓圈］
02:19
[Red circle]
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或許你可以在紅色圈圈出現的
02:21
Maybe you're the type of person
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02:23
who claps the millisecond
after a red circle appears.
after a red circle appears.
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千分之一秒內拍手，
02:26
Or maybe you're the type of person
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也或許你是那種寧可多花點時間
02:27
who takes just a little bit longer
to be 100 percent sure.
to be 100 percent sure.
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百分百肯定後才出手的人。
又或許你在綠色圈出現
就拍手，違反了規則。
就拍手，違反了規則。
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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最棒的一點在於這個測驗
和一般的測試不同，
和一般的測試不同，
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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一般測試會區分某些人適合
這工作，而某些人不是。
這工作，而某些人不是。
02:36
where some people are employable
and some people aren't.
and some people aren't.
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但多元測試卻是去辨別
你的特質適合什麼，
你的特質適合什麼，
02:39
Instead it's about understanding
the fit between your characteristics
the fit between your characteristics
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以及你能勝任某項工作的特長為何。
02:42
and what would make you
good a certain job.
good a certain job.
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研究顯示如果你在出現紅圈時拍手，
而從沒在綠圈時誤拍，
而從沒在綠圈時誤拍，
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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02:48
you might be high in attentiveness
and high in restraint.
and high in restraint.
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那麼你有著相當高的
專注力及自制力，
專注力及自制力，
02:51
People in that quadrant tend to be
great students, great test-takers,
great students, great test-takers,
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這類的人通常會是好學生，
測試也能得到好成績，
測試也能得到好成績，
02:55
great at project management or accounting.
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適合當專案管理者或從事會計工作。
02:57
But if you clap immediately on red
and sometimes clap on green,
and sometimes clap on green,
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如果你在紅圈圈出現時立即拍手，
偶爾在綠色出現時也不小心拍手，
偶爾在綠色出現時也不小心拍手，
03:00
that might mean that
you're more impulsive and creative,
you're more impulsive and creative,
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表示你有可能比較
隨興而為，也較有創意，
隨興而為，也較有創意，
03:03
and we've found that top-performing
salespeople often embody these traits.
salespeople often embody these traits.
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我們發現頂尖業務
通常具有這些特徵。
通常具有這些特徵。
03:07
The way we actually use this in hiring
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我們之所以能將
這項測試運用在聘僱上，
這項測試運用在聘僱上，
03:09
is we have top performers in a role
go through neuroscience exercises
go through neuroscience exercises
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是因為我們讓在該領域表現傑出的人
實際做過神經科學的測驗，
實際做過神經科學的測驗，
03:12
like this one.
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就像這個。
03:14
Then we develop an algorithm
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根據結果，我們發展出一套演算公式
03:15
that understands what makes
those top performers unique.
those top performers unique.
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以了解是哪一項特質
讓優秀的人才脫穎而出。
讓優秀的人才脫穎而出。
03:18
And then when people apply to the job,
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因而人們在求職時，
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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我們才能篩選出最適任的人。
03:24
So you might be thinking
there's a danger in this.
there's a danger in this.
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也許你在想：這樣的測試也有風險，
03:26
The work world today
is not the most diverse
is not the most diverse
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因為今日的職場並沒有太多元化，
03:28
and if we're building algorithms
based on current top performers,
based on current top performers,
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如果只針對現有優秀的工作者
特質來設計演算公式，
特質來設計演算公式，
03:32
how do we make sure
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那麼要如何確保
03:33
that we're not just perpetuating
the biases that already exist?
the biases that already exist?
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我們不會讓現有的偏差
一再地重複發生？
一再地重複發生？
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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假設我們的演算法是以
頂尖執行長為設計基礎，
頂尖執行長為設計基礎，
03:40
and use the S&P 500 as a training set,
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並以標準普爾 500 家公司為訓練集，
03:43
you would actually find
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則會發現
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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選出來的人大概都會是叫做
約翰的白人男性而少有女性，
約翰的白人男性而少有女性，
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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那是因為在現實職場中，
擔任該職位的都是這類型的人。
擔任該職位的都是這類型的人。
03:51
But technology actually poses
a really interesting opportunity.
a really interesting opportunity.
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在這裡科技就能提供
另一個有趣的機會，
另一個有趣的機會，
03:54
We can create algorithms
that are more equitable
that are more equitable
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我們可以做出一套更公正，
03:57
and more fair than human beings
have ever been.
have ever been.
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而且比人類更公平的演算系統。
03:59
Every algorithm that we put
into production has been pretested
into production has been pretested
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每套演算法在實際應用前
都需經過前置測試，
都需經過前置測試，
04:03
to ensure that it doesn't favor
any gender or ethnicity.
any gender or ethnicity.
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以確保不會偏好某性別或種族。
04:06
And if there's any population
that's being overfavored,
that's being overfavored,
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如果系統真有偏重某些族群，
04:08
we can actually alter the algorithm
until that's no longer true.
until that's no longer true.
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那麼我們可以改變演算方法，
直到情況改善。
直到情況改善。
04:12
When we focus on the inherent
characteristics
characteristics
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當我們著重在發掘某人與生俱來、
04:14
that can make somebody
a good fit for a job,
a good fit for a job,
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使他在職場上適任的人格特質，
04:16
we can transcend racism,
classism, sexism, ageism --
classism, sexism, ageism --
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我們就能夠超越種族、
階級、性別、年齡，
階級、性別、年齡，
04:20
even good schoolism.
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甚至名校的偏見。
04:21
Our best technology and algorithms
shouldn't just be used
shouldn't just be used
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我們這樣棒的科技
和演算法不應該只用在
和演算法不應該只用在
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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追電影或尋找小賈斯汀的新歌上面。
04:28
Imagine if we could harness
the power of technology
the power of technology
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而是應該要駕馭科技，
04:31
to get real guidance
on what we should be doing
on what we should be doing
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並根據我們的內在潛質
04:33
based on who we are at a deeper level.
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來引導我們要追求的目標。
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