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  • Applying for jobs online

    網路上求職

  • is one of the worst digital experiences of our time.

    是現代最糟糕的一種數位體驗,

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

    但親自求職也好不了多少。

  • [The Way We Work]

    【我們的工作方式】

  • Hiring as we know it is broken on many fronts.

    我們所知的招聘方式 在很多方面存在缺陷,

  • It's a terrible experience for people.

    對很多人來說都是難受的體驗。

  • About 75 percent of people

    過去一年中,

  • who applied to jobs using various methods in the past year

    以不同方式找工作的求職者裡面

  • said they never heard anything back from the employer.

    有 75% 的人表示從未得到雇主回覆。

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

    而對招聘的公司來說, 情況也沒好到哪裡。

  • 46 percent of people get fired or quit

    任職不到一年

  • within the first year of starting their jobs.

    就被解聘或辭職的人也高達 46%,

  • It's pretty mind-blowing.

    實在令人震驚,

  • It's also bad for the economy.

    也不利於經濟發展。

  • For the first time in history,

    第一次在歷史上出現了

  • we have more open jobs than we have unemployed people,

    職位空缺多於失業人數的現象,

  • and to me that screams that we have a problem.

    這是個令人不容小覷的問題。

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

    我認為所有問題的關鍵在於 那一張紙——也就是履歷表。

  • A résumé definitely has some useful pieces in it:

    履歷表固然有不少有用訊息:

  • what roles people have had, computer skills,

    例如求職者曾經擔任的職位、 他們的電腦技能,

  • what languages they speak,

    及他們會的語言。

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

    但履歷表無法顯示求職者的潛能,

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

    因為他們過去沒有機會 去擔任能展現長才的工作。

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

    隨着經濟急促轉型, 網上湧現大批職缺

  • that might require skills that nobody has,

    需要一些無前例可循的技能。

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

    如果我們單看求職者過去的成就,

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

    則無法為未來的職位找到合適人才。

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

    因此我認為科技在這方面能幫上很多忙。

  • You've probably seen that algorithms have gotten pretty good

    大家或許見識過演算法能針對需求

  • at matching people to things,

    為人們找到適合的東西。

  • but what if we could use that same technology

    那麼是否我們可以將相同的技術

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

    應用在尋找適合的職缺呢?

  • But I know what you're thinking.

    我知道大家在想什麼,

  • Algorithms picking your next job sounds a little bit scary,

    用演算法來媒合工作聽起來有點可怕,

  • but there is one thing that has been shown

    但有一項技術能夠預測

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

    求職者在新工作上的成就,

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

    那就是所謂的「多元測試」。

  • Multimeasure tests really aren't anything new,

    多元測試並不是什麼新玩意兒,

  • but they used to be really expensive

    以前它的成本很高,

  • and required a PhD sitting across from you

    需要一位博士坐在你面前,

  • and answering lots of questions and writing reports.

    回答一大堆問題、寫一堆報告。

  • Multimeasure tests are a way

    多元測試能了解

  • to understand someone's inherent traits --

    一個人與生俱有的特色,

  • your memory, your attentiveness.

    例如:你的記憶力、注意力。

  • What if we could take multimeasure tests

    如果我們可以運用多元測試,

  • and make them scalable and accessible,

    讓它可量身訂做、普及,

  • and provide data to employers about really what the traits are

    並將這些數據提供給雇主, 以個人特質來篩選

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

    真的適合這項工作的人選呢?

  • This all sounds abstract.

    這聽起來很抽象。

  • Let's try one of the games together.

    不如,我們來玩個小遊戲。

  • You're about to see a flashing circle,

    遊戲中你會看到一個圓圈閃過,

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

    如果你看到紅色圓圈, 就要立刻拍手,

  • and do nothing when it's green.

    如果是綠的,就不要做任何動作。

  • [Ready?]

    [準備好了沒?]

  • [Begin!]

    [開始!]

  • [Green circle]

    [綠色圓圈]

  • [Green circle]

    [綠色圓圈]

  • [Red circle]

    [紅色圓圈]

  • [Green circle]

    [綠色圓圈]

  • [Red circle]

    [紅色圓圈]

  • Maybe you're the type of person

    或許你可以在紅色圈圈出現的

  • who claps the millisecond after a red circle appears.

    千分之一秒內拍手,

  • Or maybe you're the type of person

    也或許你是那種寧可多花點時間

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

    百分百肯定後才出手的人。

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

    又或許你在綠色圈出現 就拍手,違反了規則。

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

    最棒的一點在於這個測驗 和一般的測試不同,

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

    一般測試會區分某些人適合 這工作,而某些人不是。

  • Instead it's about understanding the fit between your characteristics

    但多元測試卻是去辨別 你的特質適合什麼,

  • and what would make you good a certain job.

    以及你能勝任某項工作的特長為何。

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

    研究顯示如果你在出現紅圈時拍手, 而從沒在綠圈時誤拍,

  • you might be high in attentiveness and high in restraint.

    那麼你有著相當高的 專注力及自制力,

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

    這類的人通常會是好學生, 測試也能得到好成績,

  • great at project management or accounting.

    適合當專案管理者或從事會計工作。

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

    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,

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

    表示你有可能比較 隨興而為,也較有創意,

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

    我們發現頂尖業務 通常具有這些特徵。

  • The way we actually use this in hiring

    我們之所以能將 這項測試運用在聘僱上,

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

    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,

  • like this one.

    就像這個。

  • Then we develop an algorithm

    根據結果,我們發展出一套演算公式

  • that understands what makes those top performers unique.

    以了解是哪一項特質 讓優秀的人才脫穎而出。

  • And then when people apply to the job,

    因而人們在求職時,

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

    我們才能篩選出最適任的人。

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

    也許你在想:這樣的測試也有風險,

  • The work world today is not the most diverse

    因為今日的職場並沒有太多元化,

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

    如果只針對現有優秀的工作者 特質來設計演算公式,

  • how do we make sure

    那麼要如何確保

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

    我們不會讓現有的偏差 一再地重複發生?

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

    假設我們的演算法是以 頂尖執行長為設計基礎,

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

    並以標準普爾 500 家公司為訓練集,

  • you would actually find

    則會發現

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

    選出來的人大概都會是叫做 約翰的白人男性而少有女性,

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

    那是因為在現實職場中, 擔任該職位的都是這類型的人。

  • But technology actually poses a really interesting opportunity.

    在這裡科技就能提供 另一個有趣的機會,

  • We can create algorithms that are more equitable

    我們可以做出一套更公正,

  • and more fair than human beings have ever been.

    而且比人類更公平的演算系統。

  • Every algorithm that we put into production has been pretested

    每套演算法在實際應用前 都需經過前置測試,

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

    以確保不會偏好某性別或種族。

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

    如果系統真有偏重某些族群,

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

    那麼我們可以改變演算方法, 直到情況改善。

  • When we focus on the inherent characteristics

    當我們著重在發掘某人與生俱來、

  • that can make somebody a good fit for a job,

    使他在職場上適任的人格特質,

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

    我們就能夠超越種族、 階級、性別、年齡,

  • even good schoolism.

    甚至名校的偏見。

  • Our best technology and algorithms shouldn't just be used

    我們這樣棒的科技 和演算法不應該只用在

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

    追電影或尋找小賈斯汀的新歌上面。

  • Imagine if we could harness the power of technology

    而是應該要駕馭科技,

  • to get real guidance on what we should be doing

    並根據我們的內在潛質

  • based on who we are at a deeper level.

    來引導我們要追求的目標。

Applying for jobs online

網路上求職

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B1 中級 中文 美國腔 TED 圓圈 測試 演算 演算法 工作

【TED】Priyanka Jain:如何讓申請工作不那麼痛苦(How to make applying for jobs less painful | The Way We Work,TED系列)。 (【TED】Priyanka Jain: How to make applying for jobs less painful (How to make applying for jobs less painful | The Way We Work, a TED series))

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    林宜悉 發佈於 2021 年 01 月 14 日
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