【中英双语】高情商的人工智能，可以让人变得更聪明吗？
Can AI Nudge Us to Make Better Choices?
The behavioral revolution in economics was triggered by a simple, haunting question: what if people don’t act rationally? This same question now vexes the technology field.
经济学上有一个简单但却绕不过去也回避不了的问题，那就是：如果人们的行为不理性时，该怎么办？这个看似简单的问题却是引发经济学行为革命的主因。
In the online world, once expected to be a place of ready information and easy collaboration, lies and hate can spread faster than truth and kindness. Corporate systems, too, elicit irrational behavior. For example, when predicting sales, employees often hide bad deals and selectively report the good ones.
在网络世界中，谎言和仇恨可以比真相和善意传播得更快，而网络世界曾经被认为是一个随时准备好信息和容易合作的地方。企业制度也会引发非理性行为。例如，在预测销售额时，员工经常隐藏不好的交易，有选择地报告好的交易。
AI stands at the crossroads of the behavioral question, with the potential to make matters worse or to elicit better outcomes from us. The key to better outcomes is to boost AI’s emotional quotient — its EQ. How? By training algorithms to mimic the way people behave in constructive relationships.
现在，人工智能站在了行为问题的十字路口，它有可能会让人们的行为变得更加糟糕，但也有可能会变得更好。要想让人工智能取得好的结果，最关键的就是提高它的情感商数——也就是情商。那么如何才能提高它的情商呢？有一种方法是运用多项运算法则去模仿人类在关系构建中的行为方式。
Whether or not we care to admit it, we build relationships with apps. And apps, like people, can elicit both positive and negative behaviors from us. When people with high EQ interact with us, they learn our patterns, empathize with our motivations, and carefully weigh their responses. They decide to ignore, challenge, or encourage us depending on how they anticipate we will react.
不管我们承认与否，现在的我们已经和多种应用程序建立了各种关系。而应用程序就像人一样可以做出来自我们自己的积极和消极的行为。高情商的人与我们往来时，他们知道我们的模式，理解我们的动机，并仔细权衡他们自己该做出怎样的反应。是忽视我们？挑战我们？或者是鼓励我们？这些都取决于他们是如何预测我们将要发生的反应。
AI can be trained to do the same thing. Why? Because behaviors are more predictable than we like to think. The $70 billion weight-loss industry thrives because diet companies know that most people regain lost weight. The $40 billion casino industry profits from gamblers’ illogical hope of a comeback. Credit card companies know it is hard for people to break their spending habits.
人工智能也可以被训练做同样的事情。为什么呢? 因为这些行为比我们想象的更容易预测。市值700亿美元的减肥行业之所以蓬勃发展，是因为减肥公司知道大多数人的体重都会反弹的。市值400亿美元的赌博业知道赌徒们会一味妄图东山再起的。他们因赌徒们这个不合逻辑的想法而从中获利。银行推出各种信用卡是因为他们知道人们很难改变消费习惯。
While it’s still quite early, the fields of behavioral science and machine learning already provide some promising techniques for creating higher-EQ AI that organizations are putting to work to produce better outcomes. Those techniques include:
虽然要做到这一步还为时尚早，但行为科学领域和机器学习领域已经为创造高情商人工智能提供了一些很有前景的技术。很多公司也正在为此着手创造更好的产品。这些有前景的技术包括:
Noting pattern breaks and nudging. People who know you can easily tell when you are breaking a pattern and react accordingly. For example, a friend may notice that you suddenly changed your routine and ask you why. The Bank of America online bill paying system similarly notes pattern breaks to prevent user keying errors. The system remembers the pattern of payments you’ve made in the past and posts an alert if you substantially increase your payment to a vendor.
注意惯用模式的打破并作出提醒。现实中，熟悉你的人往往很容易就能判断出你惯用的模式什么时候被打破了，他们会对你模式的打破做出相应的反应。例如，你朋友注意到了你突然改变了日常习惯，就会问你为什么跟原来不一样了。美国银行的在线账单支付系统就是采用这种类似的模式，以防止用户输入错误。系统会记住你过去的付款方式。如果某天你大幅增加了对供应商的付款，系统便会发出警告。
Encouraging self-awareness with benchmarks. Bluntly telling individuals they are performing poorly often backfires, provoking defensiveness rather than greater effort. A more diplomatic method simply allows people to see how they compare with others. For instance, a major technology firm used AI to generate more accurate sales forecasts than the sales team did. To induce the team to course-correct, the system provides each team member with personalized visualizations showing how their forecasts differ from the AI forecast. A simple nudge then inquires why this might be the case. The team member can provide a rational explanation, avoid providing feedback, or claim that the AI is incorrect. The AI learns about the substance and timing of the individual’s reaction, weighs it against the gap in the two forecasts, and can choose an appropriate second-order nudge.
用基准鼓励员工自我反省。直言不讳地指出他人表现很差往往会火上浇油，这种方式不但起不到作用，还会激起他们的抵触情绪，造成适得其反的效果。一个较为灵活的方法就是让他们自己看到与他人相比较后的结果如何。例如，一家大型科技公司采用人工智能做出比销售团队更准确的销售预测。为了纠正团队成员的认识，该系统为每个团队成员提供个性化的视觉展示，显示他们自己的预测与人工智能的预测之间的差异。这个简单的操作就能反映出员工出现这种情况的原因。员工们可以对此提供合理的解释，避免假设性的意见，或者称AI的信息不正确。人工智能了解了个体反应的实质和时间，权衡两种预测之间的差距，然后选择一个合适的二级推手。
Using game theory to accept or challenge conclusions. Imagine being on a team that must find errors in over 100,000 mutual fund transactions a day. A fund managing a trillion dollars in assets is tackling this daunting problem with AI. The first version of the AI scored potential errors (called “anomalies”) by risk and potential cost, then queued the riskiest anomalies first. The system then tracked the time the analyst spent on each anomaly. It was assumed that analysts would spend more time on the risker anomalies and less time on the “no-brainers.” In fact, some analysts were flying through the riskiest anomalies, reaching suspiciously fast conclusions.
运用博弈论来接受或挑战结论。试想一下，如果你所在的团队每天必须在基金交易中找出10万笔以上的错误，你该如何应对？有一个管理万亿美元资产的基金正在用人工智能解决这个令人望而生畏的问题。这个人工智能最初版本是通过风险和潜在成本来计算潜在错误(又称为“异常”)，然后先找出最危险的异常。系统再跟踪分析人员花费在异常方面的时间。那时的假设是分析师会在风险异常上花更多的时间，在“容易的事情”上花更少的时间。但实际上，一些分析师对风险最大的异常现象会蜻蜓点水般掠过，从而得出了让人怀疑的快速结论。
In most massive screening systems, the rate of false positives is often extremely high. For example, secret teams from the Department of Homeland Security found that the TSA failed to stop 95% of inspectors’ attempts to smuggle weapons or explosive materials through screening. Mutual fund analysts scouring countless transactions, like TSA screeners dealing with thousands of passengers, their eyes glazing over, simply glide over anomalies.
很多规模很大的审查系统中，误报率往往非常高。例如，美国国土安全部的一个秘密小组发现，运输安全管理局95%的检查人员未能通过审查阻止武器的走私或爆炸物的出现。国际货币基金的分析师们审视着数不清的交易，他们就像美国运输安全管理局的安检人员处理成千上万的乘客时一样，虽然眼睛睁得大大的，但是对异常情况却总是视而不见。
The fund is tackling this dangerous, though highly predictable, behavior with an algorithm employed by chess playing programs. This modified version of sequential game theory first monitors whether the analyst concludes that an anomaly is a false positive or decides to spend more time on it. The AI, playing the role of a chess opponent, can decide to counter by accepting the analyst’s decision or challenging it.
国际货币基金组织正在利用国际象棋程序中使用的一种算法来处理这种危险的行为。这个博弈论的修正版本首先监视分析人员是否会认为异常是一种假象，他是否会决定要不要花费更多的时间在异常方面。国际象棋中扮演对手角色的人工智能机器就可以通过接受分析师的决定或挑战来决定反击。
Choosing the right time for insight and action. By any standard, Jeff Bezos is a master decision maker. In a recent interview with Bloomberg TV’s David Rubenstein, he described his framework for making decisions. When approached about a complex decision late in the afternoon he often replies, “That doesn’t sound like a 4 o’clock decision; that sounds like a 9 o’clock [in the morning] decision.”
为洞察和行动选择正确的时间。无论以何种标准衡量，杰夫•贝佐斯都是一位决策大师。最近在接受彭博电视记者大卫•鲁宾斯坦的采访中，贝佐斯讲述了他的决策框架。如果在下午晚些时候问他一个复杂的决定时，他通常会如此回答:“这听起来不像是下午4点钟的决定；听起来像是早上9点的决定。”所以时间很重要。
My firm’s sales team A/B tested the right time of day to maximize responses to prospecting emails and found a dramatic difference in response rates between messages sent Tuesday morning and Friday afternoon. Many consumer messaging systems are tuned to maximize yield. The tuning algorithm can be enhanced to determine the type of decision to be made and the tendency of users to respond and make better choices. For example, decisions that need more thought could be presented at a time when the decision maker has more time to think — either through prediction or by the user’s scheduling.
我公司的销售团队A队和B队测试了一天中最合适最有效地回复潜在客户邮件的时间，发现周二上午和周五下午发送邮件的回复率有很大的不同。许多消费者的信息系统都进行了调优以达到最大化收益。优化算法可以提高消费者要做出的决策类型以及做出更好选择的趋势。例如，需要花时间思考的决策可以在决策者时间充足的时候提出，这样的决策要么被通过，要么就会被列入计划之内。
Could higher-EQ AI help bring more civility to the internet? Social media companies might do well to consider a distinction Western business people soon learn when negotiating with their Japanese counterparts — “honne” (what one feels inside) versus “tatemae” (what one publicly expresses). A shared understanding of the distinction between what one feels and what one is expected to say leads to fewer miscalculations. An algorithm based on that distinction might conceivably be developed to address the predictable tendencies of people to say and do things under the influence of crowds (even if virtual ones) that they would otherwise hesitate to do. Someone preparing an inflammatory, misleading, or cruel post might be nudged to reconsider their language or to notice the mob-like tenor of a “trending” topic. The challenges of developing such emotionally charged, high-EQ AI are daunting, but instead of simply weeding out individual posts it might ultimately be more beneficial to change online behavior for the better.
高情商的人工智能能给互联网带来更多的文明吗?社交媒体公司最好考虑一下西方商界人士在与日本同行谈判时学到的一个区别——“honne”(内心的感受)和“tatemae”(公开表达的感受)。明白一个人的感觉和他想要说的话之间的区别能省去不少的误判。基于这一区别的算法可能会被开发出来，因为这有助于解决人们在他人的影响下(即使是虚拟的人群)犹豫不决想说和想做的事情的可预测倾向。想写煽动性、误导性或粗俗性的帖子的人可能会被人工智能敦促重新组织他们的语言，或者注意到热门话题中那些键盘侠们。开发这种充满感情、高情商的人工智能的挑战是艰巨的，但与其简单地删除个别帖子，不如从根本上解决问题。
Bob Suh是OnCorps的创始人兼首席执行官。OnCorps是一家致力于提高决策科学的机器学习公司。Bob Suh在加入OnCorps之前，是埃森哲公司的首席技术策略师，也是该公司全球科技业务的首席战略官。
阿丫丫|译 周强|校
