How to make applying for jobs less painful | The Way We Work, a TED series

Applying for jobs online

譯者: Sailin Lu 審譯者: Bruce Sung

is one of the worst digital experiences of our time.

And applying for jobs in person really isn't much better.

[The Way We Work]

Hiring as we know it is broken on many fronts.

網路上求職

It's a terrible experience for people.

是現代最糟糕的一種數位體驗，

About 75 percent of people

但親自求職也好不了多少。

who applied to jobs using various methods in the past year

【我們的工作方式】

said they never heard anything back from the employer.

And at the company level it's not much better.

我們所知的招聘方式 在很多方面存在缺陷，

46 percent of people get fired or quit

對很多人來說都是難受的體驗。

within the first year of starting their jobs.

過去一年中，

以不同方式找工作的求職者裡面

It's pretty mind-blowing.

It's also bad for the economy.

For the first time in history,

有 75% 的人表示從未得到雇主回覆。

we have more open jobs than we have unemployed people,

而對招聘的公司來說， 情況也沒好到哪裡。

and to me that screams that we have a problem.

任職不到一年

I believe that at the crux of all of this is a single piece of paper: the résumé.

就被解聘或辭職的人也高達 46%，

實在令人震驚，

A résumé definitely has some useful pieces in it:

也不利於經濟發展。

what roles people have had, computer skills,

第一次在歷史上出現了

職位空缺多於失業人數的現象，

what languages they speak,

but what it misses is what they have the potential to do

這是個令人不容小覷的問題。

我認為所有問題的關鍵在於 那一張紙——也就是履歷表。

that they might not have had the opportunity to do in the past.

And with such a quickly changing economy where jobs are coming online

履歷表固然有不少有用訊息：

that might require skills that nobody has,

例如求職者曾經擔任的職位、 他們的電腦技能，

if we only look at what someone has done in the past,

及他們會的語言。

但履歷表無法顯示求職者的潛能，

we're not going to be able to match people to the jobs of the future.

因為他們過去沒有機會 去擔任能展現長才的工作。

So this is where I think technology can be really helpful.

隨着經濟急促轉型， 網上湧現大批職缺

You've probably seen that algorithms have gotten pretty good

需要一些無前例可循的技能。

at matching people to things,

but what if we could use that same technology

如果我們單看求職者過去的成就，

則無法為未來的職位找到合適人才。

to actually help us find jobs that we're really well-suited for?

因此我認為科技在這方面能幫上很多忙。

But I know what you're thinking.

Algorithms picking your next job sounds a little bit scary,

大家或許見識過演算法能針對需求

but there is one thing that has been shown

為人們找到適合的東西。

to be really predictive of someone's future success in a job,

那麼是否我們可以將相同的技術

and that's what's called a multimeasure test.

應用在尋找適合的職缺呢？

Multimeasure tests really aren't anything new,

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，

but they used to be really expensive

and required a PhD sitting across from you

但有一項技術能夠預測

and answering lots of questions and writing reports.

求職者在新工作上的成就，

Multimeasure tests are a way

那就是所謂的「多元測試」。

to understand someone's inherent traits --

多元測試並不是什麼新玩意兒，

your memory, your attentiveness.

以前它的成本很高，

What if we could take multimeasure tests

需要一位博士坐在你面前，

and make them scalable and accessible,

回答一大堆問題、寫一堆報告。

and provide data to employers about really what the traits are

多元測試能了解

一個人與生俱有的特色，

of someone who can make them a good fit for a job?

例如：你的記憶力、注意力。

This all sounds abstract.

Let's try one of the games together.

如果我們可以運用多元測試，

You're about to see a flashing circle,

讓它可量身訂做、普及，

and your job is going to be to clap when the circle is red

並將這些數據提供給雇主， 以個人特質來篩選

and do nothing when it's green.

真的適合這項工作的人選呢？

[Ready?]

[Begin!]

這聽起來很抽象。

不如，我們來玩個小遊戲。

[Green circle]

遊戲中你會看到一個圓圈閃過，

[Green circle]

如果你看到紅色圓圈， 就要立刻拍手，

[Red circle]

[Green circle]

如果是綠的，就不要做任何動作。

[Red circle]

［準備好了沒？］

Maybe you're the type of person

［開始！］

who claps the millisecond after a red circle appears.

［綠色圓圈］

Or maybe you're the type of person

［綠色圓圈］

who takes just a little bit longer to be 100 percent sure.

［紅色圓圈］

［綠色圓圈］

Or maybe you clap on green even though you're not supposed to.

［紅色圓圈］

The cool thing here is that this isn't like a standardized test

或許你可以在紅色圈圈出現的

千分之一秒內拍手，

where some people are employable and some people aren't.

Instead it's about understanding the fit between your characteristics

也或許你是那種寧可多花點時間

百分百肯定後才出手的人。

and what would make you good a certain job.

又或許你在綠色圈出現 就拍手，違反了規則。

We found that if you clap late on red and you never clap on the green,

最棒的一點在於這個測驗 和一般的測試不同，

you might be high in attentiveness and high in restraint.

一般測試會區分某些人適合 這工作，而某些人不是。

但多元測試卻是去辨別 你的特質適合什麼，

People in that quadrant tend to be great students, great test-takers,

以及你能勝任某項工作的特長為何。

great at project management or accounting.

研究顯示如果你在出現紅圈時拍手， 而從沒在綠圈時誤拍，

But if you clap immediately on red and sometimes clap on green,

that might mean that you're more impulsive and creative,

那麼你有著相當高的 專注力及自制力，

and we've found that top-performing salespeople often embody these traits.

這類的人通常會是好學生， 測試也能得到好成績，

The way we actually use this in hiring

適合當專案管理者或從事會計工作。

is we have top performers in a role go through neuroscience exercises

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，

like this one.

表示你有可能比較 隨興而為，也較有創意，

Then we develop an algorithm

that understands what makes those top performers unique.

我們發現頂尖業務 通常具有這些特徵。

And then when people apply to the job,

we're able to surface the candidates who might be best suited for that job.

我們之所以能將 這項測試運用在聘僱上，

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，

So you might be thinking there's a danger in this.

就像這個。

The work world today is not the most diverse

根據結果，我們發展出一套演算公式

以了解是哪一項特質 讓優秀的人才脫穎而出。

and if we're building algorithms based on current top performers,

因而人們在求職時，

how do we make sure

that we're not just perpetuating the biases that already exist?

我們才能篩選出最適任的人。

For example, if we were building an algorithm based on top performing CEOs

也許你在想：這樣的測試也有風險，

因為今日的職場並沒有太多元化，

and use the S&P 500 as a training set,

如果只針對現有優秀的工作者 特質來設計演算公式，

you would actually find

that you're more likely to hire a white man named John than any woman.

那麼要如何確保

我們不會讓現有的偏差 一再地重複發生？

And that's the reality of who's in those roles right now.

假設我們的演算法是以 頂尖執行長為設計基礎，

But technology actually poses a really interesting opportunity.

並以標準普爾 500 家公司為訓練集，

We can create algorithms that are more equitable

則會發現

and more fair than human beings have ever been.

選出來的人大概都會是叫做 約翰的白人男性而少有女性，

Every algorithm that we put into production has been pretested

那是因為在現實職場中， 擔任該職位的都是這類型的人。

to ensure that it doesn't favor any gender or ethnicity.

在這裡科技就能提供 另一個有趣的機會，

And if there's any population that's being overfavored,

我們可以做出一套更公正，

we can actually alter the algorithm until that's no longer true.

而且比人類更公平的演算系統。

每套演算法在實際應用前 都需經過前置測試，

When we focus on the inherent characteristics

that can make somebody a good fit for a job,

以確保不會偏好某性別或種族。

we can transcend racism, classism, sexism, ageism --

如果系統真有偏重某些族群，

even good schoolism.

那麼我們可以改變演算方法， 直到情況改善。

Our best technology and algorithms shouldn't just be used

for helping us find our next movie binge or new favorite Justin Bieber song.

當我們著重在發掘某人與生俱來、

使他在職場上適任的人格特質，

Imagine if we could harness the power of technology

我們就能夠超越種族、 階級、性別、年齡，

to get real guidance on what we should be doing

甚至名校的偏見。

based on who we are at a deeper level.

我們這樣棒的科技 和演算法不應該只用在

追電影或尋找小賈斯汀的新歌上面。

而是應該要駕馭科技，

並根據我們的內在潛質

來引導我們要追求的目標。