社交媒體你點的贊 老闆可都在看

Liking Nicki Minaj on Facebook may not seem like a momentous decision — but one day, it could help determine whether you get hired. A new study suggests that based on your Facebook likes, a computer model can predict your personality better than your friends — and in some ways, know more about your life than you do. This also means anyone who can see your Facebook profile could one day learn about your personality, and make determinations about your future job performance, your creditworthiness and more.

在Facebook上“贊”妮琪·米娜(Nicki Minaj)似乎並不是一個重大決定——但有一天，它有可能將決定你是否會被錄用。一項新研究表明，計算機模型能夠根據你在Facebook上點的“贊”來推測你的個性，甚至比朋友的判斷還準確——而且從某種程度而言，它比你自己更瞭解你的生活。這也意味着，任何能夠看到你Facebook主頁的人，有一天或許都能瞭解你的個性，並且判斷你未來的工作表現、你的信譽度以及其他不少東西。

Some fear that personality research will open up yet another front in the continuing battle over data privacy online. But could it also help ordinary users win that battle — or at least understand what they’re up against?

有人擔心，個性研究將爲關於網絡數據隱私的持久戰開闢另一條陣線。不過，它是否也能幫助普通用戶打贏這場戰爭——或者至少讓他們明白自己面對的是什麼呢？

For a paper published in Proceedings of the National Academy of Sciences, Wu Youyou, Michal Kosinski and David Stillwell used a computer model to gauge subjects’ personalities based on Facebook Likes. To measure the model’s accuracy, the researchers compared its verdicts to subjects’ ratings of their own personalities. The result: Fed enough Likes, computers are quite good at judging human personality — better than the average friend or co-worker, and about as good as the average spouse. At least when it comes to a certain conception of personality (the researchers used the five-factor model, which looks at traits like extroversion and neuroticism), a computer program can know you as well as your husband or wife does.

吳悠悠（音）、米夏爾·科辛斯基(Michal Kosinski)和戴維·史迪威(David Stillwell)在《美國國家科學院院刊》(Proceedings of the National Academy of Sciences)上發表了一篇論文。他們利用一個計算機模型，根據研究對象在Facebook上點的“贊”來對他們的個性進行評估。爲了衡量模型的準確性，研究人員把計算機的結論與研究對象對自己性格的評定進行了比較。結果是：如果能夠蒐集到足夠的點“贊”數據，計算機就能很好地判斷人的個性——比一般的朋友或同事的判斷更加準確，幾乎與一般配偶的水平相當。至少在某種個性維度方面（研究人員採用的是五因素模型，它研究的是外傾性和神經質等特徵），計算機程序對人的瞭解能達到丈夫或妻子的水平。

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The researchers also tested the computer model’s assessments to see how good they were at predicting 13 “life outcomes” that have been linked to personality, including health, political leanings and satisfaction with life. The model’s ratings were better than those provided by other humans at predicting all but one of these outcomes (life satisfaction). And they were better than people’s self-ratings of their personality at predicting four of the outcomes: Facebook use; number of Facebook friends; use of alcohol, tobacco, and drugs; and field of study.

研究人員還測試了計算機模型的評估結果，看它們能否準確預測與個性相關的13種“人生境遇”，其中包括健康、政治傾向和生活滿意度。除一個指標以外（生活滿意度），這個模型對所有指標的預測結果都比旁人更加準確。而且在預測四個指標方面，它的表現比人們的自我判斷還要更好。這四個方面分別是：Facebook的使用，Facebook的好友人數，飲酒、抽菸和吸毒的行爲，以及研究領域。

The first two aren’t necessarily shocking, said Ms. Wu in an interview — you’d expect a Facebook-based algorithm to be able to predict Facebook behavior accurately. More surprising, she explained, is the fact that computers’ personality ratings were so good at predicting how much people drank or used drugs, and what subject they were likely to study. Using the computer model to guess at such outcomes is “basically a measure of how the judgment of personality described this person’s behavior in real life,” she said. “In that sense, computers to some extent know you better than people know themselves.”

吳悠悠在採訪中表示，前兩個指標並不驚人——基於Facebook的算法肯定能更精確地判斷與Facebook有關的行爲。她解釋道，更令人意外的是，計算機的個性評估結果竟然能如此準確地預測人們飲酒或吸毒的程度，以及他們可能研究什麼科目。利用計算機模型來猜測這些指標，“基本上就是一種如何用個性判斷來描述此人在現實生活中的行爲的方法，”她說。“從這個角度來看，計算機在一定程度上比人們更瞭解自己。”

Computer-based personality assessment could have a number of real-world uses, said Ms. Wu. Marketers could use the information (with users’ consent) to fine-tune their ads or reach out to certain groups: “A bungee-jumping company,” for example, “might want to target people who are open to new experience.” It could change online dating: Rather than asking daters to fill out site-specific questionnaires, “we can just take your digital records and make predictions about your characteristics and personality and try to pair you up with other people who are similar to you.” The model could also be used in job recruitment, perhaps making a better match between people and careers than companies are currently finding.

吳悠悠說，基於電腦的個性評估可能有一系列實際用途。市場營銷人員可以利用這些信息（在徵得用戶同意的情況下）來調整自己的廣告或者觸及特定的人羣：例如，“蹦極公司或許想把目標鎖定在願意接受新體驗的人羣”。它也會改變網上約會：以後尋找約會對象的人不再需要在特定的網頁上填寫調查問卷，“我們可以用你的數碼記錄，判斷你的特點和個性，然後嘗試讓你與相似的人配對。”這個模型也可以用在招聘當中，或許能讓人員與工作進行更好的配對，而且能比企業目前做得更好。

Dr. Kosinski, one of Ms. Wu’s co-authors, also sees computer personality testing as a possible recruitment tool. It has “the potential to completely change how we see the job market,” he said in an interview. Each person could get a computer-generated personality profile, and then prospective employers could search through the profiles for people whose personalities and skills matched their needs. Instead of posting a job and interviewing applicants, “you basically reach out to two or three people that match your profile.”

吳悠悠的合著作者科辛斯基也認爲，計算機個性測試有成爲招聘工具的潛力。他在採訪中說，它“有可能完全改變我們對就業市場的看法”。每個人都能獲得一份計算機生成的個性資料，然後準僱主就可以通過搜索這些資料，尋找個性和技能滿足他們需求的人。你不用再發布招聘廣告，對應聘者進行面試，“只需聯繫兩三個資料匹配的人即可”。

He’s not the first to suggest a broader role for data analysis in the hiring process — and that suggestion has inspired some concern. In an Atlantic analysis of what he describes as “the application of predictive analytics to people’s careers,” Don Peck asks:

他並不是第一個想讓數據分析在招聘過程中發揮更大作用的人——這種想法引發了一些擔憂。唐·佩克(Don Peck)在《大西洋月刊》(The Atlantic)中分析了他所謂的“預測性分析的職場應用”，他問道：

“Should job candidates be ranked by what their Web habits say about them? Should the ‘data signature’ of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.”

“我們應該根據應聘者的網絡習慣所反映出的東西，來對他們進行評判嗎？作爲天生的領導者的‘數據特徵’，是否應該成爲升職的參考？這些都是如今的現實問題，它們也帶來了嚴重的擔憂：我們將把最微妙、最人性的技能——對他人的天賦和未來進行評判——讓給機器；模型可能會出錯；有些人永遠無法在新的勞力大軍中獲得機會。”

And Danielle Citron, a law professor who has studied privacy, worries that data on people’s personalities could be stored and used in contexts they never expected. “What concerns me,” she said in an interview, “is the potential for keeping people’s assessments and scores in ways that have a much more lasting effect, can be merged, and then analyzed and propagated in ways that aren’t accountable.”

曾研究隱私的法學教授丹妮爾·西特魯恩(Danielle Citron)擔心，人們的個性數據會被人儲存下來，用在他們意想不到的情況中。“我擔心的是，”她在採訪中說，“人們的評估結果和得分被保存下來，可能產生更持久的影響，可能會被人整合，然後被分析和傳播，且無法追究任何人的負責。”

Personality assessments don’t just reveal positive attributes, she noted — “there’s also people whose personalities may have some negative implications, like they’re very absent-minded or they have short attention spans.” And if computerized personality screening and data collection become widespread, such people could lose out on jobs, be denied bank loans or even be flagged for extra security at airports. “It’s not always a good story for everybody,” she said.

她指出，個性評估不只會透露積極特徵——“還有些人的個性或許包含一些消極的東西，比如非常健忘，或者注意力不容易集中”。如果基於計算機的個性分析和數據蒐集得到普遍應用，這些人就會丟掉飯碗，貸款時被銀行拒絕，甚至會進入某些名單，在機場時需要接受額外的安全檢查。她說，“它並不總是一件好事。”

Dr. Citron believes limits on the use of personality data may not be sufficient to stop it from harming us — we may need to stop it from being gathered in the first place. She noted that the United States government used census data to target Japanese-Americans for internment during World War II. “If we’re going to rely on the use restrictions, those give way to times of crisis.” Instead, she said, “maybe we need to think about limits on collection.” And personality data may be “the sort of thing we don’t want employers to ever collect.”

西特魯恩認爲限制個性數據的使用可能不足以阻止這種行爲危害我們——我們或許需要從一開始就阻止信息的收集。她指出，美國政府在二戰期間利用人口普查數據鎖定他們將要拘禁的日裔美國人。“如果我們依靠使用限制，當出現危機時，這些限制是沒有用的。”她表示，“或許我們需要考慮對數據收集加以限制。”個性信息或許是“那種我們不希望僱主蒐集到的信息”。

Dr. Kosinski agrees that Facebook-based personality assessment presents privacy concerns. “With a psychological assessment that is automated and based on a digital footprint, anyone could potentially assess your personality without asking your permission,” he said.

科辛斯基認同這種說法，即基於Facebook的個性評估帶來了隱私憂患。他表示，“有了基於數字足跡自動生成的心理評估，任何人都有可能在沒有獲得你的許可的情況下，評估你的個性。”

However, he said, if we are concerned about online privacy, Facebook shouldn’t necessarily be our biggest worry. Your Facebook activities “are the least potentially dangerous types of digital footprint” from his perspective. “Your Internet service operator, your government, a bunch of marketing companies — they’re recording all the websites you’re visiting. Your credit card company records all the purchases you’re making and when and where and what did you buy and how much you paid for it. Your mobile phone operator records places you go to, whom you talk to, how much time did you spend talking with them.” You’d have to get rid of your credit card and phone to escape such data collection, he said.

但他表示，如果我們擔心網絡隱私，Facebook不應該是我們最大的問題。他認爲，在Facebook上的活動“是潛在危險最小的數字足跡”“互聯網服務運營商、政府，以及一些營銷公司，都在記錄你訪問過的所有網站。信用卡公司記錄了你的每一筆消費，以及你的消費時間、地點、物品及金額。手機運營商會記錄你到過的地方，你交談的對象及交談時間。”他表示，你得放棄使用信用卡和手機，才能避免數據被收集。

His advice: “Use those technologies as much as you can, but also exert pressure on the decision makers and policy makers to design policies that will basically be protecting you in this environment.” Regulations, he said, “should give people full control over their personal data.”

他的建議是：“儘可能多地使用這些科技，同時也向決策者和政策制定者施加壓力，要求制定出基本上可以在這種環境中保護你的政策。”他說，監管機構“應該讓人們可以充分掌控自己的個人資料”。

But Scott R. Peppet, a law professor who also studies privacy issues, suggests that even control may not be sufficient, if not enough people exercise it. Even if revealing your information to an employer is technically voluntary, he said in an interview, if enough people do it, those who don’t may be at a disadvantage. “Let’s say employers routinely started asking for your Facebook information because they wanted to be able to look at your Likes and assess your personality, and you’re the one person in the group who says no,” he said. At a certain point, “the fact that you won’t reveal it is itself revealing about you, and people start to draw inferences based on that refusal.”

但同樣研究隱私問題的法學教授斯科特·R·帕佩特(Scott R. Peppet)表示，即使獲得了這種控制權，如果沒有足夠多的人行使它，可能還是有問題。他在接受採訪時表示，即使向僱主透露信息本身是純屬自願，但如果有很多人這樣做，那麼不這麼做的人就可能處於劣勢。“比方說，僱主詢問你的Facebook信息，因爲他們希望能看看你點讚的東西，據此評估你的個性，而在一羣人中，只有你拒絕，”他說。從某種程度上說，“你的拒絕本身就揭示了你的一些特點，人們就會開始根據這種拒絕來進行推論了。”

He agrees with Dr. Kosinski that Facebook may be only the beginning. “There’s probably lots of inputs that we’re going to show over the next few years correlate or predict or assess personality,” he explained, from your Fitbit stats to your iTunes downloads. “In a world where lots of things reveal lots of things about you, it’s not so clear if you’re going to know which one you should or shouldn’t do to protect your privacy.”

他同意科辛斯基博士的看法：Facebook可能只是一個開始。從你的Fitbit統計數據到iTunes下載信息，“將會有大量輸入被用來推測、預測或評估個性，我們將在未來幾年裏進行展示。”他解釋道。“如有很多東西可以揭示大量關於你的事情，你是否會知道該做哪些事，不該做哪些事，來保護自己的隱私，這一點還不是很清楚。”

Dr. Peppet isn’t optimistic about future legal protections: “The likelihood of large-scale federal privacy regulation or a new privacy statute seems pretty low to me at the moment.” But studies like the one Ms. Wu and Dr. Kosinski conducted may at least draw attention to the issue: Such research, he said, is “making people realize that there are policy implications here that need to be seriously considered. I’m not sure what format that’s going to take, but I do think there’s increasing policy interest in, ‘what uses can these kinds of inferences be put to, and what uses are just too creepy?’”

帕佩特博士不看好未來的法律保護：“我覺得，眼前要進行大規模的聯邦隱私監管，或者制定一個新的隱私法規，可能性似乎非常之低。”但像吳女士和科辛斯基博士進行的那種研究至少可以提醒大家注意這個問題：他說這樣的研究就是“讓人們意識到，這方面存在着需要認真考慮的政策問題。我不知道它們會採取什麼樣的形式，但我確實認爲，對於‘這些類型的推論可以用來做什麼，而哪些用途太過分了？’，人們在政策上的興趣正在增加”。

And the study’s focus on Facebook activity may be a strength. “I like this study because Facebook Likes seem kind of innocuous,” he said. “You just Like your friend’s picture of their kid’s Halloween costume.” What Ms. Wu and her team have shown, he said, is that something “seemingly very innocent really does reveal a lot about us.”

該研究的重點放在Facebook的活動上，這可能是一個優勢。“我喜歡這種研究，因爲Facebook上的點贊看起來無傷大雅，”他說。“你給朋友的照片，給他們小孩的萬聖節服裝點贊。”他說，吳女士及其團隊的研究表明，“看似平常無奇的東西確實揭示了有關我們的很多事情。”