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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“赞”妮琪·米娜(Nicki Minaj)似乎并不是一个重大决定——但有一天，它有可能将决定你是否会被录用。一项新研究表明，计算机模型能够根据你在Facebook上点的“赞”来推测你的个性，甚至比朋友的判断还准确——而且从某种程度而言，它比你自己更了解你的生活。这也意味着，任何能够看到你Facebook主页的人，有一天或许都能了解你的个性，并且判断你未来的工作表现、你的信誉度以及其他不少东西。
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?
有人担心，个性研究将为关于网络数据隐私的持久战开辟另一条阵线。不过，它是否也能帮助普通用户打赢这场战争——或者至少让他们明白自己面对的是什么呢？
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.
吴悠悠（音）、米夏尔·科辛斯基(Michal Kosinski)和戴维·史迪威(David Stillwell)在《美国国家科学院院刊》(Proceedings of the National Academy of Sciences)上发表了一篇论文。他们利用一个计算机模型，根据研究对象在Facebook上点的“赞”来对他们的个性进行评估。为了衡量模型的准确性，研究人员把计算机的结论与研究对象对自己性格的评定进行了比较。结果是：如果能够搜集到足够的点“赞”数据，计算机就能很好地判断人的个性——比一般的朋友或同事的判断更加准确，几乎与一般配偶的水平相当。至少在某种个性维度方面（研究人员采用的是五因素模型，它研究的是外倾性和神经质等特征），计算机程序对人的了解能达到丈夫或妻子的水平。
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.
研究人员还测试了计算机模型的评估结果，看它们能否准确预测与个性相关的13种“人生境遇”，其中包括健康、政治倾向和生活满意度。除一个指标以外（生活满意度），这个模型对所有指标的预测结果都比旁人更加准确。而且在预测四个指标方面，它的表现比人们的自我判断还要更好。这四个方面分别是：Facebook的使用，Facebook的好友人数，饮酒、抽烟和吸毒的行为，以及研究领域。
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.”
吴悠悠在采访中表示，前两个指标并不惊人——基于Facebook的算法肯定能更精确地判断与Facebook有关的行为。她解释道，更令人意外的是，计算机的个性评估结果竟然能如此准确地预测人们饮酒或吸毒的程度，以及他们可能研究什么科目。利用计算机模型来猜测这些指标，“基本上就是一种如何用个性判断来描述此人在现实生活中的行为的方法，”她说。“从这个角度来看，计算机在一定程度上比人们更了解自己。”
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.
吴悠悠说，基于电脑的个性评估可能有一系列实际用途。市场营销人员可以利用这些信息（在征得用户同意的情况下）来调整自己的广告或者触及特定的人群：例如，“蹦极公司或许想把目标锁定在愿意接受新体验的人群”。它也会改变网上约会：以后寻找约会对象的人不再需要在特定的网页上填写调查问卷，“我们可以用你的数码记录，判断你的特点和个性，然后尝试让你与相似的人配对。”这个模型也可以用在招聘当中，或许能让人员与工作进行更好的配对，而且能比企业目前做得更好。
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.”
吴悠悠的合著作者科辛斯基也认为，计算机个性测试有成为招聘工具的潜力。他在采访中说，它“有可能完全改变我们对就业市场的看法”。每个人都能获得一份计算机生成的个性资料，然后准雇主就可以通过搜索这些资料，寻找个性和技能满足他们需求的人。你不用再发布招聘广告，对应聘者进行面试，“只需联系两三个资料匹配的人即可”。
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:
他并不是第一个想让数据分析在招聘过程中发挥更大作用的人——这种想法引发了一些担忧。唐·佩克(Don Peck)在《大西洋月刊》(The Atlantic)中分析了他所谓的“预测性分析的职场应用”，他问道：
“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.”
“我们应该根据应聘者的网络习惯所反映出的东西，来对他们进行评判吗？作为天生的领导者的‘数据特征’，是否应该成为升职的参考？这些都是如今的现实问题，它们也带来了严重的担忧：我们将把最微妙、最人性的技能——对他人的天赋和未来进行评判——让给机器；模型可能会出错；有些人永远无法在新的劳力大军中获得机会。”
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.”
曾研究隐私的法学教授丹妮尔·西特鲁恩(Danielle Citron)担心，人们的个性数据会被人储存下来，用在他们意想不到的情况中。“我担心的是，”她在采访中说，“人们的评估结果和得分被保存下来，可能产生更持久的影响，可能会被人整合，然后被分析和传播，且无法追究任何人的负责。”
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，个性评估不只会透露积极特征——“还有些人的个性或许包含一些消极的东西，比如非常健忘，或者注意力不容易集中”。如果基于计算机的个性分析和数据搜集得到普遍应用，这些人就会丢掉饭碗，贷款时被银行拒绝，甚至会进入某些名单，在机场时需要接受额外的安全检查。她说，“它并不总是一件好事。”
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.”
西特鲁恩认为限制个性数据的使用可能不足以阻止这种行为危害我们——我们或许需要从一开始就阻止信息的收集。她指出，美国政府在二战期间利用人口普查数据锁定他们将要拘禁的日裔美国人。“如果我们依靠使用限制，当出现危机时，这些限制是没有用的。”她表示，“或许我们需要考虑对数据收集加以限制。”个性信息或许是“那种我们不希望雇主搜集到的信息”。
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.
科辛斯基认同这种说法，即基于Facebook的个性评估带来了隐私忧患。他表示，“有了基于数字足迹自动生成的心理评估，任何人都有可能在没有获得你的许可的情况下，评估你的个性。”
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，如果我们担心网络隐私，Facebook不应该是我们最大的问题。他认为，在Facebook上的活动“是潜在危险最小的数字足迹”“互联网服务运营商、政府，以及一些营销公司，都在记录你访问过的所有网站。信用卡公司记录了你的每一笔消费，以及你的消费时间、地点、物品及金额。手机运营商会记录你到过的地方，你交谈的对象及交谈时间。”他表示，你得放弃使用信用卡和手机，才能避免数据被收集。
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.”
他的建议是：“尽可能多地使用这些科技，同时也向决策者和政策制定者施加压力，要求制定出基本上可以在这种环境中保护你的政策。”他说，监管机构“应该让人们可以充分掌控自己的个人资料”。
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.”
但同样研究隐私问题的法学教授斯科特·R·帕佩特(Scott R. Peppet)表示，即使获得了这种控制权，如果没有足够多的人行使它，可能还是有问题。他在接受采访时表示，即使向雇主透露信息本身是纯属自愿，但如果有很多人这样做，那么不这么做的人就可能处于劣势。“比方说，雇主询问你的Facebook信息，因为他们希望能看看你点赞的东西，据此评估你的个性，而在一群人中，只有你拒绝，”他说。从某种程度上说，“你的拒绝本身就揭示了你的一些特点，人们就会开始根据这种拒绝来进行推论了。”
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可能只是一个开始。从你的Fitbit统计数据到iTunes下载信息，“将会有大量输入被用来推测、预测或评估个性，我们将在未来几年里进行展示。”他解释道。“如有很多东西可以揭示大量关于你的事情，你是否会知道该做哪些事，不该做哪些事，来保护自己的隐私，这一点还不是很清楚。”
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来的法律保护：“我觉得，眼前要进行大规模的联邦隐私监管，或者制定一个新的隐私法规，可能性似乎非常之低。”但像吴女士和科辛斯基博士进行的那种研究至少可以提醒大家注意这个问题：他说这样的研究就是“让人们意识到，这方面存在着需要认真考虑的政策问题。我不知道它们会采取什么样的形式，但我确实认为，对于‘这些类型的推论可以用来做什么，而哪些用途太过分了？’，人们在政策上的兴趣正在增加”。
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.”
该研究的重点放在Facebook的活动上，这可能是一个优势。“我喜欢这种研究，因为Facebook上的点赞看起来无伤大雅，”他说。“你给朋友的照片，给他们小孩的万圣节服装点赞。”他说，吴女士及其团队的研究表明，“看似平常无奇的东西确实揭示了有关我们的很多事情。”
