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

Applying for jobs online

翻译人员: duan JiGang 校对人员: jacks peng

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.

在线申请工作

It's a terrible experience for people.

是我们这个时代最糟糕的 数字化体验之一。

About 75 percent of people

面对面交谈也没好到哪儿去。

who applied to jobs using various methods in the past year

【我们的工作方式】

said they never heard anything back from the employer.

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

众所周知，招聘方式 在很多方面一团糟。

46 percent of people get fired or quit

对人们来说这是一个糟糕的经历。

within the first year of starting their jobs.

在过去一年

使用多种方式申请工作时的 群体中，大约有75%的人

It's pretty mind-blowing.

It's also bad for the economy.

For the first time in history,

说他们从未收到雇主的任何反馈。

we have more open jobs than we have unemployed people,

对公司来说，这不是一件好事情。

and to me that screams that we have a problem.

在开始工作的不到一年时间里，

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

46%的人被解雇或者主动离职。

这一点很令人震惊。

A résumé definitely has some useful pieces in it:

这种现象对经济也产生了负面影响。

what roles people have had, computer skills,

在历史上第一次，

招聘岗位超过了无业人员的人数，

what languages they speak,

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

对我而言，这意味着出问题了。

我认为这一切的关键在于一张纸： 简历。

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

毫无疑问，简历中包含着一些 有用的信息：

that might require skills that nobody has,

人们扮演过哪些角色， 有哪些计算机技能，

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

精通什么语言，

但并未提及他们有哪方面的潜力，

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

这些事情他们在过去可能没机会去做。

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

在变化如此迅速的经济环境中， 在线发布的工作机会

You've probably seen that algorithms have gotten pretty good

可能要求的都是没人掌握的技术，

at matching people to things,

如果我们只看一个人过去做了什么，

but what if we could use that same technology

就不能把这个人和 未来的工作匹配起来。

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

所以我认为这是技术真正有用的地方。

But I know what you're thinking.

Algorithms picking your next job sounds a little bit scary,

您可能已经看到了算法如何很好的

but there is one thing that has been shown

把人和事物匹配起来，

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

但是如果我们把同样的技术用于

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

真正帮助找到那些为 我们量身打造的工作昵？

Multimeasure tests really aren't anything new,

我知道你在想什么。

让算法为你挑拣下一份工作 听起来有点离谱，

but they used to be really expensive

and required a PhD sitting across from you

但有个东西已经被证明

and answering lots of questions and writing reports.

能够成功预测某人 是否能胜任未来的工作，

Multimeasure tests are a way

这就是所谓的多评估测试。

to understand someone's inherent traits --

多评估测试并不是什么新概念，

your memory, your attentiveness.

但是它们曾经价格不菲，

What if we could take multimeasure tests

并且需要一个博士坐在你对面，

and make them scalable and accessible,

回答一堆问题并且整理成报告。

and provide data to employers about really what the traits are

多评估测试是一种用来

理解某人内在特质的方法——

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

你的记忆力，你的专注力。

This all sounds abstract.

Let's try one of the games together.

如果我们能够做多评估测试，

You're about to see a flashing circle,

让公众都可以参与，

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

并且把相关数据提供给雇主， 比如某个人的某些特质

and do nothing when it's green.

使其真的很适合这个工作，会怎样？

[Ready?]

[Begin!]

这些听起来很抽象。

让我们一起试试其中一个游戏。

[Green circle]

你将要看到一个闪烁的圆，

[Green circle]

你的任务就是当圆是红色时鼓掌，

[Red circle]

[Green circle]

当圆是绿色时什么也不做。

[Red circle]

【准备好了？】

Maybe you're the type of person

【开始】

who claps the millisecond after a red circle appears.

【绿色圆】

Or maybe you're the type of person

【绿色圆】

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

【红色圆】

【绿色圆】

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

【红色圆】

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

或许你是那种

在红色圆出现后毫秒内鼓掌的人。

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

或者你是另外一种人，

Instead it's about understanding the fit between your characteristics

那种需要多花点时间， 等到100%确认才行动的人。

and what would make you good a certain job.

或者你在还不确定时 就为绿色圆鼓掌。

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

很酷的一点是这并不 像是个标准的测试，

you might be high in attentiveness and high in restraint.

那种决定能被雇佣与否的测试。

相反，这是个理解你的特性和

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

适合你的工作之间的匹配度测试。

great at project management or accounting.

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

我们发现如果你在红色时鼓掌晚， 而在绿色时从不鼓掌，

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

你可能具备高度专注力， 能够很好的自我约束。

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

在那个象限的人们 往往擅长学习和考试，

The way we actually use this in hiring

精于项目管理和财会。

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

如果你在红色时立刻鼓掌， 并且有时在绿色鼓掌，

like this one.

那意味着你可能易冲动 并且具备创造性，

Then we develop an algorithm

that understands what makes those top performers unique.

我们发现顶级的商人 经常会表现出这些特质。

And then when people apply to the job,

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

我们在招聘中使用它的方式是

我们让角色中表现出色的人参与

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

类似的神经科学训练。

然后我们开发了一个算法

The work world today is not the most diverse

来理解是什么让这些 表现出众者脱颖而出。

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

然后当人们申请工作的时候，

how do we make sure

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

我们就会优先列出 最适合那项工作的候选人。

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

你可能在思考其中存在的风险。

当今的职场多样性仍有待提高，

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

如果我们基于当下的 出众员工构建算法，

you would actually find

要怎样确保

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

我们不是在固守既有的偏见呢？

例如，如果我们基于顶尖表现的 CEO构建一个算法

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

But technology actually poses a really interesting opportunity.

并且使用S&P500作为一个训练集，

We can create algorithms that are more equitable

你将会发现

and more fair than human beings have ever been.

更可能雇佣一个叫约翰的 白人男子而非任何女性。

Every algorithm that we put into production has been pretested

这是目前谁正处在这个角色的现实。

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

但是技术实际上给出了 一个真正有趣的机会。

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

我们可以创造一些比人类 任何时候都更平等

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

和更公正的算法。

每一个我们投入生产的 算法都会被预先进行测试

When we focus on the inherent characteristics

that can make somebody a good fit for a job,

以确保它不会偏爱任何性别 或者种族。

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

如果有任何人群正在被过度偏爱，

even good schoolism.

我们可以调整算法直到该现象消失。

Our best technology and algorithms shouldn't just be used

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

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，

Imagine if we could harness the power of technology

我们可以超越种族，阶级， 性别和老龄化主义——

to get real guidance on what we should be doing

甚至是名校背景。

based on who we are at a deeper level.

我们最好的技术和算法不应该只用于

帮助寻找我们的下一个卖座电影 或者贾斯汀·比伯的新歌。

想象一下如果我们能够利用技术的 力量，

在更深层次上理解我们是谁， 并得到一个

我们应该做什么的真正指引会怎样。