未來面試要刷臉測(cè)性格英語美文

　　So-called emotion recognition technology is in its infancy. But artificial intelligence companies claim it has the power to transform recruitment.

　　所謂的情感識(shí)別技術(shù)還處于早期發(fā)展階段。但是人工智能公司聲稱，這項(xiàng)技術(shù)完全有可能改變招聘的模式。

　　Their algorithms, they say, can decipher how enthusiastic, bored or honest a job applicant may be — and help employers weed out candidates with undesirable characteristics. Employers, including Unilever, are already beginning to use the technology.

　　這些公司指出，他們的算法可以解讀求職者有多熱情、多厭煩或多誠實(shí)——并且?guī)椭�雇主排除性格不太合適的應(yīng)聘者。包括聯(lián)合利華(Unilever)在內(nèi)的雇主已經(jīng)開始使用這項(xiàng)技術(shù)。

　　London-based Human, founded in 2016, is a start-up that analyses video-based job applications. The company claims it can spot the emotional expressions of prospective candidates and match them with personality traits — information its algorithms collect by deciphering subliminal facial expressions when the applicant answers questions.

　　互曼公司(Human)是倫敦的一家初創(chuàng)公司，于2016年成立。該公司主要對(duì)求職者提交的視頻材料進(jìn)行分析。該公司聲稱，它可以發(fā)現(xiàn)潛在候選人的情感表達(dá)，并將其與他們的性格特征——其算法通過對(duì)求職者回答問題時(shí)下意識(shí)的面部表情進(jìn)行解讀所收集的信息——進(jìn)行比對(duì)。

　　Human sends a report to the recruiter detailing candidates’ emotional reactions to each interview question, with scores against characteristics that specify how “honest” or “passionate” an applicant is.

　　互曼會(huì)向招聘公司發(fā)送報(bào)告，詳細(xì)說明應(yīng)聘者對(duì)面試中每個(gè)問題的情緒反應(yīng)，通過對(duì)照其性格特征給出評(píng)分，用于反映申請(qǐng)人的“誠實(shí)度”或“熱情度”。

　　“If [the recruiter] says, ‘We are looking for the most curious candidate,’ they can find that person by comparing the candidates’ scores,” says Yi Xu, Human’s founder and chief executive.

　　互曼的創(chuàng)始人兼首席執(zhí)行官Yi Xu指出：“如果(招聘公司)說，’我們?cè)趯ふ液闷嫘奶貏e強(qiáng)的人’，他們可以通過比較各候選人的得分來找到合適的人�！�

　　Recruiters can still assess candidates at interview in the conventional way, but there is a limit to how many they can meet or the number of video applications they can watch. Ms Xu says her company’s emotion recognition technology helps employers screen a larger pool of candidates and shortlist people they may not have considered otherwise.

　　招聘公司仍然可以采用傳統(tǒng)的面試方式來評(píng)估候選人，但他們可以面談的人數(shù)或觀看視頻申請(qǐng)的數(shù)量是有限的。Yi Xu說，互曼公司的情感識(shí)別技術(shù)可以幫助雇主篩查更多的候選人，并篩選出他們通過其他面試方式可能不會(huì)考慮的候選人。

　　“An interviewer will have bias, but [with technology] they don’t judge the face but the personality of the applicant,” she says. One aim, she claims, is to overcome ethnic and gender discrimination in recruitment.

　　她說：“面試官會(huì)有偏見，但(采用技術(shù)后)，他們就不會(huì)依據(jù)外表來評(píng)判申請(qǐng)人，而是會(huì)依據(jù)他們的性格�！彼�聲稱，這項(xiàng)技術(shù)的目標(biāo)之一就是克服招聘過程的種族和性別歧視。

　　The algorithms of Affectiva and Human are based at least partially on Facs. A specialist first labels the emotions of hundreds or thousands of images (videos are analysed frame by frame), before letting an algorithm process them — the training phase.

　　Affectiva和互曼這兩家公司的算法都至少部分基于FACS系統(tǒng)。一位專家首先要對(duì)成百上千張圖像(視頻分析需要一幀一幀地進(jìn)行)中人臉?biāo)�流露的情緒進(jìn)行標(biāo)記，然后讓算法進(jìn)行處理——這是訓(xùn)練階段。

　　During training, the algorithm is watched to see how closely it predicts emotions compared with the manual labelling done by the Facs specialist. Errors are taken into account and the model adjusts itself. The process is repeated with other labelled images until the error is minimised.

　　在訓(xùn)練過程中，要對(duì)算法進(jìn)行觀察，看看其對(duì)情緒的預(yù)測(cè)結(jié)果與FACS專家所做的手動(dòng)標(biāo)記有多接近。模型會(huì)根據(jù)發(fā)現(xiàn)的錯(cuò)誤自行進(jìn)行調(diào)整。用其他已標(biāo)記的圖像重復(fù)這一過程，直到差錯(cuò)降到盡可能低的水平。

　　Once the training is done, the algorithm can be introduced to images it has never seen and it makes predictions based on its training.

　　訓(xùn)練完成后，可以用算法來觀察其從未見過的圖像，并根據(jù)之前的訓(xùn)練進(jìn)行預(yù)測(cè)。

　　Frederike Kaltheuner, policy adviser on data innovation at Privacy International, a global campaigning organisation, agrees that human interviewers can be biased. But she says: “new systems bring new problems”.

　　“隱私國(guó)際”(Privacy International)數(shù)據(jù)創(chuàng)新方面的政策顧問弗雷德里克.卡爾特霍伊納(Frederike Kaltheuner)同意人類面試官可能會(huì)有偏見，但她說：“新系統(tǒng)會(huì)帶來新問題”。

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