Applying for jobs online is one of the worst digital experiences of our time. And applying for jobs in person really isn't much better.
網路上求職 是現代最糟糕的一種數位體驗, 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. It's a terrible experience for people. About 75 percent of people who applied to jobs using various methods in the past year said they never heard anything back from the employer. And at the company level it's not much better. 46 percent of people get fired or quit within the first year of starting their jobs. It's pretty mind-blowing. It's also bad for the economy. For the first time in history, we have more open jobs than we have unemployed people, and to me that screams that we have a problem.
我們所知的招聘方式 在很多方面存在缺陷, 對很多人來說都是難受的體驗。 過去一年中, 以不同方式找工作的求職者裡面 有 75% 的人表示從未得到雇主回覆。 而對招聘的公司來說, 情況也沒好到哪裡。 任職不到一年 就被解聘或辭職的人也高達 46%, 實在令人震驚, 也不利於經濟發展。 第一次在歷史上出現了 職位空缺多於失業人數的現象, 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: the résumé. A résumé definitely has some useful pieces in it: what roles people have had, computer skills, what languages they speak, but what it misses is what they have the potential to do 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 that might require skills that nobody has, if we only look at what someone has done in the past, we're not going to be able to match people to the jobs of the future.
我認為所有問題的關鍵在於 那一張紙——也就是履歷表。 履歷表固然有不少有用訊息: 例如求職者曾經擔任的職位、 他們的電腦技能, 及他們會的語言。 但履歷表無法顯示求職者的潛能, 因為他們過去沒有機會 去擔任能展現長才的工作。 隨着經濟急促轉型, 網上湧現大批職缺 需要一些無前例可循的技能。 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。
So this is where I think technology can be really helpful. You've probably seen that algorithms have gotten pretty good at matching people to things, but what if we could use that same technology to actually help us find jobs that we're really well-suited for? But I know what you're thinking. Algorithms picking your next job sounds a little bit scary, but there is one thing that has been shown to be really predictive of someone's future success in a job, and that's what's called a multimeasure test.
因此我認為科技在這方面能幫上很多忙。 大家或許見識過演算法能針對需求 為人們找到適合的東西。 那麼是否我們可以將相同的技術 應用在尋找適合的職缺呢? 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, 但有一項技術能夠預測 求職者在新工作上的成就, 那就是所謂的「多元測試」。
Multimeasure tests really aren't anything new, but they used to be really expensive and required a PhD sitting across from you and answering lots of questions and writing reports. Multimeasure tests are a way to understand someone's inherent traits -- your memory, your attentiveness. What if we could take multimeasure tests and make them scalable and accessible, and provide data to employers about really what the traits are of someone who can make them a good fit for a job?
多元測試並不是什麼新玩意兒, 以前它的成本很高, 需要一位博士坐在你面前, 回答一大堆問題、寫一堆報告。 多元測試能了解 一個人與生俱有的特色, 例如:你的記憶力、注意力。 如果我們可以運用多元測試, 讓它可量身訂做、普及, 並將這些數據提供給雇主, 以個人特質來篩選 真的適合這項工作的人選呢?
This all sounds abstract. Let's try one of the games together. You're about to see a flashing circle, and your job is going to be to clap when the circle is red and do nothing when it's green.
這聽起來很抽象。 不如,我們來玩個小遊戲。 遊戲中你會看到一個圓圈閃過, 如果你看到紅色圓圈, 就要立刻拍手, 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 who claps the millisecond after a red circle appears. Or maybe you're the type of person who takes just a little bit longer to be 100 percent sure. Or maybe you clap on green even though you're not supposed to. The cool thing here is that this isn't like a standardized test where some people are employable and some people aren't. Instead it's about understanding the fit between your characteristics and what would make you good a certain job. We found that if you clap late on red and you never clap on the green, you might be high in attentiveness and high in restraint. People in that quadrant tend to be great students, great test-takers, great at project management or accounting. But if you clap immediately on red and sometimes clap on green, that might mean that you're more impulsive and creative, and we've found that top-performing salespeople often embody these traits.
或許你可以在紅色圈圈出現的 千分之一秒內拍手, 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。 又或許你在綠色圈出現 就拍手,違反了規則。 最棒的一點在於這個測驗 和一般的測試不同, 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, 以及你能勝任某項工作的特長為何。 研究顯示如果你在出現紅圈時拍手, 而從沒在綠圈時誤拍, 那麼你有著相當高的 專注力及自制力, 這類的人通常會是好學生, 測試也能得到好成績, 適合當專案管理者或從事會計工作。 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, 表示你有可能比較 隨興而為,也較有創意, 我們發現頂尖業務 通常具有這些特徵。
The way we actually use this in hiring is we have top performers in a role go through neuroscience exercises like this one. Then we develop an algorithm that understands what makes those top performers unique. 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, 就像這個。 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。 因而人們在求職時, 我們才能篩選出最適任的人。
So you might be thinking there's a danger in this. The work world today is not the most diverse and if we're building algorithms based on current top performers, how do we make sure that we're not just perpetuating the biases that already exist? For example, if we were building an algorithm based on top performing CEOs and use the S&P 500 as a training set, you would actually find that you're more likely to hire a white man named John than any woman. And that's the reality of who's in those roles right now. But technology actually poses a really interesting opportunity. We can create algorithms that are more equitable and more fair than human beings have ever been. Every algorithm that we put into production has been pretested to ensure that it doesn't favor any gender or ethnicity. And if there's any population that's being overfavored, we can actually alter the algorithm until that's no longer true. When we focus on the inherent characteristics that can make somebody a good fit for a job, we can transcend racism, classism, sexism, ageism -- even good schoolism.
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, 如果只針對現有優秀的工作者 特質來設計演算公式, 那麼要如何確保 我們不會讓現有的偏差 一再地重複發生? 假設我們的演算法是以 頂尖執行長為設計基礎, 並以標準普爾 500 家公司為訓練集, 則會發現 選出來的人大概都會是叫做 約翰的白人男性而少有女性, 那是因為在現實職場中, 擔任該職位的都是這類型的人。 在這裡科技就能提供 另一個有趣的機會, 我們可以做出一套更公正, 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, 以確保不會偏好某性別或種族。 如果系統真有偏重某些族群, 那麼我們可以改變演算方法, 直到情況改善。 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, 我們就能夠超越種族、 階級、性別、年齡, 甚至名校的偏見。
Our best technology and algorithms shouldn't just be used for helping us find our next movie binge or new favorite Justin Bieber song. Imagine if we could harness the power of technology to get real guidance on what we should be doing 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。