人工智能招聘：提升多元化的四種方式
Anne Fisher
2019-06-10
圖片來源：portishead1—Getty Images/iStockphoto
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人工智能是一個“黑匣子”——神秘而且令人生畏。同時，人工智能技術新的排列組合如雨后春筍般層出不窮，在招聘領域尤其如此。然而，盡管雇主希望自己的員工組成更加多元包容，但人工智能行業本身卻因為幾乎都是白人男性而受到抨擊。例如，紐約大學研究人員最近的一項研究指出，在像Facebook和谷歌這樣的科技巨頭中，女性和有色族裔員工的占比非常小，整個企業都面臨著“多元化危機”。
諷刺的是，如果能夠正確使用人工智能，它們“非常有希望在決策方面比人類做得更好，特別是在招聘工作中。”亞歷山德拉·莫伊西洛維奇說。莫伊西洛維奇是IBM的人工智能研究員，擁有16項機器學習專利，協助開發了可以用于檢查其他算法是否存在無意偏差的算法。她指出，想要利用人工智能鼓勵多元化，重要的一點是要確保構建黑匣子的團隊本身就是一個多元化的團隊，確保這個團隊擁有不同背景和不同觀點。
“人工智能工具是否優秀，是否公正，取決于我們輸入的數據。”莫伊西洛維奇說，“人工智能不是要取代人類的智慧，而是進行補充。”
人工智能可以幫助雇主找到并吸引來自于不同性別、年齡和種族的新員工。以下是四種主要方式：
人工智能知道如何針對最優秀的候選人宣傳
招聘信息中的文字表述很重要，不僅因為文字表述經常無意中阻止一些潛在的雇員申請空缺崗位。“我們人類只能盡最大努力猜測怎么說會引起求職者的共鳴，但經常猜錯。”人工智能公司Textio的聯合創始人及首席執行官基蘭·斯奈德說。
Textio公司用了大約5億個真實招聘廣告的數據，讓人工智能分析這些廣告在現實中得到的回應，建議公司應該使用和避免使用哪些詞語。例如，對于客戶eBay而言，“原先的經驗”這一短語使男性申請人增加了50％。“但是，‘表現出來的能力’卻多吸引了40％的女性，即使它和‘原先的經驗’說的差不多是同一件事。”斯奈德說。
不同性別、種族、民族中性的語言“變化很快。沒有‘請使用這10個詞’的詞匯清單。”她補充道，“但恰當的詞可以在合適的時機吸引最多元化的申請者。”
人工智能擴大了符合標準的申請者范圍
人工智能還能夠在鞭長莫及的區域建立更廣泛的網絡。以校園招聘為例，雇主只能派這么多人去一部分校園進行招聘——但如果完美的求職者沒有去這場人才招聘會，或干脆就去了另外一所學校呢？
人工智能公司HireVue（客戶包括英特爾、甲骨文、道瓊斯、唐恩都樂品牌等）的首席技術官洛倫·拉森表示，“非著名大學里的某名學生可能和‘正確的’大學里的學生一樣好，甚至更好，而你可能根本就不會派人去這所大學校招。”
拉森說，在過去，這名學生得不到機會。但是通過人工智能獲取潛在求職者的信息，并使用視頻聊天等現代工具，你可以輕松與他們取得聯系。“通過這種方式，可以讓更多優秀的人進入系統里，這樣你就可以‘看到’更多元化的求職群體，并進行評估。”拉森補充道。
人工智能是伯樂
簡歷是有效的招聘工具，但是“如果你專注于某位候選人簡歷上的內容，你就可能忽視很多其他人。”CareerBuilder的首席執行官伊利尼亞·諾沃謝利斯基說道，該公司的領導層現在擁有70％的女性和少數族裔，比諾沃謝利斯基在2017年加入時高了40%。
CareerBuilder網站使用人工智能幫助雇主和求職者進行最優配對，其數據庫包括超過230萬個招聘職位、1000萬個職位、13億技能點。算法完全瞄準某一工作所需的技能，找到有潛力、擁有這項技能的候選人——但這些候選人可能正在根據自己的背景申請其他工作。
“有些人簡歷中的大標題或最近一份工作不一定能夠代表他們還可以做其他什么事情。”諾沃謝利斯基說。例如，客戶服務代表需要耐心和解決問題的能力，“我們發現家庭醫療保健工作者擁有這些技能。沒有人工智能，是不可能這么配對的。”
嚴格關注技能“自然會促進多樣化，因為招聘標準對于每個候選人來說都是完全相同的，不分性別、種族、民族、年齡或其他任何因素。人工智能將所有無關緊要的東西剝離。”HireVue的洛倫·拉森說。大量的研究證實，在所謂的結構化面試中，面試人員詢問每個候選人完全相同的問題，尋找完全相同的答案，能夠最有效地消除無意識偏見。
問題是，人類面試官幾乎做不到。“我們會覺得無聊，會走神，或者突然覺得牙疼。”拉森說，“人工智能從來不會。”
人工智能可以糾正自己的偏見
人們在工作時會不自覺地帶入自己的經歷、假設和偏好，其中一些怪癖尤其難以改變，特別是當他們潛伏在潛意識中時。相比之下，即使是最聰明的機器（至少到目前為止）也只能學習和運用程序員裝進去的內容。其中可能包括，強調要歡迎所有年齡、性別和人種的最佳候選人。
“人類通常不能完全解釋自己的決定，因為我們一定程度上依靠‘直覺’。”拉森說，“但是通過算法，我們可以查明無意的偏差存在于什么地方。”
HireVue的團隊在一家客戶公司嘗試了一種算法，結果發現該算法更青睞具有深沉音色的求職者，因此，該算法在初步測試中，一直選擇男性而不是那些同樣稱職的女性。與此同時，早期其他一些人工智能系統因為在視頻采訪中偏好淺膚色的求職者而招致批評。
拉森說，程序員已經學會發現并修復類似這種情況，她還說“數據驅動技術讓我們有機會以前所未有的方式實現公平。”
這并不是說人工智能可以讓人力資源專業人士和招聘經理退位。管理公司兼容并包的政策、與有潛力的候選人建立良好關系、確保人工智能在做本職工作，這些事情只能由人來完成。
正如IBM的亞歷山德拉·莫伊西洛維奇所說：“所有的研究都表明，人類和人工智能相互配合比單打獨斗更有效。”（財富中文網） 譯者：Agatha |
Artificial intelligence can a “black box”—mysterious and more than a little intimidating. Meanwhile, new permutations of the tech are sprouting up like mushrooms, especially for recruiting and hiring. Yet as employers have increasingly tried to make their workforces more diverse and inclusive, the A.I. industry itself has taken some flak for being almost exclusively white and male. For instance, a recent study by New York University researchers points out that at tech giants like Facebook and Google, such tiny percentages of employees are female or nonwhite that the whole business is suffering a “diversity crisis.”
The irony there is that A.I., used correctly, has “a shot at being better at decision-making than we humans are, particularly in hiring,” says Aleksandra Mojsilovic. A research fellow in A.I. at IBM, Mojsilovic holds 16 patents in machine learning, and helped develop algorithms that can check other algorithms for unintended bias. An essential part of using A.I. to encourage diversity, she notes, is making sure the teams that build what goes into the black box are themselves a diverse group, with a variety of backgrounds and points of view.
“Any A.I. tool can only be as good—and as impartial—as the data we put in,” Mojsilovic says. “It’s not about replacing human intelligence, but rather about complementing it.”
A.I. has helped companies find and attract new hires of all sexes, ages, and ethnicities. Here are four main ways it’s helped them to do that:
A.I. knows how to speak to your best candidates
The words in job postings matter, not least because they often unwittingly discourage some potential hires from applying. “We as humans take our best guess at what will resonate with job seekers, but we’re often wrong,” notes Kieran Snyder, cofounder and CEO of the A.I. firm Textio.
Using a dataset of about 500 million actual job ads, and A.I. that analyzes the real-life responses they got, Textio advises companies on which words to use—and avoid. At client eBay, for instance, the phrase “prior experience” drew a 50% increase in male applicants. “But the phrase ‘demonstrated ability’—even though it means essentially the same thing—attracted 40% more women,” Snyder says.
Language that is neutral across sexes, races, and ethnicities “changes rapidly. There is no ‘use-these-10-words’ list,” she adds. “But the right word at the right moment does attract the most diverse possible group of applicants.”
A.I. widens the pool of eligible workers
A.I. also has the power to cast a wider net across unmanageable geographies. Take, for example, campus recruiting. Employers can send only so many humans to a limited number of campuses—but what if the perfect hire skipped the job fair, or goes to a different school entirely?
“A student at an obscure college where you’d never send a recruiter could be every bit as good as, or better than, graduates of the ‘right’ schools,” observes Loren Larsen, chief technology officer at A.I. firm HireVue, which lists Intel, Oracle, Dow Jones, Dunkin’ Brands, and many others among its clients.
In the old days, says Larsen, this student wouldn’t have gotten a second sniff, let alone a first. But by sourcing the leads with A.I., and using modern tools like video chatting, you can reach them with ease. “This way, a lot more people are let into the system on their merits, so you get to ‘meet’ and assess a much more diverse group of candidates,” adds Larsen.
A.I. has an eye for talent—and skill sets
Resumes are nice, but “if you focus on what it says on someone’s resume, you risk overlooking huge numbers of people,” says Irinia Novoselsky, CEO of CareerBuilder, whose top leadership is now 70% women and minorities—up from 40% when Novoselsky joined in 2017.
The site uses A.I. to help employers and job hunters find the best match, with a database that includes more than 2.3 million job postings, 10 million job titles, and 1.3 billion skills. The algorithms zero in on exactly what skills a job requires, and find promising candidates who have them—but who may, based on their background, be applying for a different job altogether.
“Someone’s resume headline or most recent role may not necessarily translate into what else they can do,” says Novoselsky. Customer service reps need, for instance, patience and problem-solving ability, and “we’ve found that home health care workers share those skills. Without A.I., making those matches would have been impossible.”
A strict focus on skills “naturally leads to more diversity, because the hiring criteria are exactly the same for each and every candidate, regardless of sex, race, ethnicity, age, or anything else. A.I. strips out all that extraneous stuff,” says Loren Larsen at HireVue. Reams of research confirm that so-called structured interviews, where interviewers ask precisely the same questions of each candidate and look for precisely the same checklist of answers, work best at eliminating unconscious biases.
The catch is, human interviewers rarely do them. “We get bored, or we’re distracted, or we have a toothache,” Larsen notes. “A.I. never does.”
A.I. can correct its own biases
People can’t help bringing their own experiences, assumptions, and preferences with them to work in the morning, and some of those quirks—especially when they lurk in the subconscious—are notoriously slow to change. By contrast, even the smartest machines (at least so far) can learn and apply only what programmers install in them. That can include an emphasis on welcoming the best-qualified candidates of all ages, sexes, and colors.
“Humans often can’t fully explain their decisions, because they’re going partly on ‘gut feel,'” says Larsen. “But with algorithms, we can pinpoint exactly where an unintentional bias has sneaked in.”
At one client company, HireVue’s team tried out an algorithm that turned out to be biased toward job applicants with deep voices so that, in preliminary testing, it kept selecting men over women who were just as qualified. Meanwhile, other, earlier A.I. systems have drawn fire for favoring light skin tones over darker ones in video interviews.
Larsen says programmers have learned to spot—and fix—that sort of thing, adding that “data-driven technology gives us the chance to keep getting more fair in ways that weren’t possible before.”
That’s not to say that A.I. can ever push human resource professionals and hiring managers to the sidelines. The tasks of managing company policy on inclusion, building great relationships with promising candidates, and making sure that A.I. is doing its job can only be done by people.
As Aleksandra Mojsilovic at IBM puts it, “All the research shows that humans and A.I., working together, are far more effective than either alone.” |
