Learning to Work with Intelligent Machines

by Matt Beane（马特·比恩）

John W. Tomac

It’s 6:30 in the morning, and Kristen is wheeling her prostate patient into the OR. She’s a senior resident, a surgeon in training. Today she’s hoping to do some of the procedure’s delicate, nerve-sparing dissection herself. The attending physician is by her side, and their four hands are mostly in the patient, with Kristen leading the way under his watchful guidance. The work goes smoothly, the attending backs away, and Kristen closes the patient by 8:15, with a junior resident looking over her shoulder. She lets him do the final line of sutures. She feels great: The patient’s going to be fine, and she’s a better surgeon than she was at 6:30.

早上六点半，克里斯汀正用轮椅把前列腺病人推进手术室。她是一名高级住院医生，即实习外科医生。今天她想亲自操刀手术中精细的神经剥离环节。主治医生站在她旁边，两个人的四只手都在病人体内操作。在主治医生的悉心指导下，克里斯汀全程主刀。手术进展顺利，主治医生退后，克里斯汀在八点一刻完成了缝合，一名初级住院医生在她身后观察学习，并完成了最后的缝合。克里斯汀感觉棒极了：病人将会康复，而她和两小时前相比，外科技艺又精进了不少。

Fast-forward six months. It’s 6:30 AM again, and Kristen is wheeling another patient into the OR, but this time for robotic prostate surgery. The attending leads the setup of a thousand-pound robot, attaching each of its four arms to the patient. Then he and Kristen take their places at a control console 15 feet away. Their backs are to the patient, and Kristen just watches as the attending remotely manipulates the robot’s arms, delicately retracting and dissecting tissue. Using the robot, he can do the entire procedure himself, and he largely does. He knows Kristen needs practice, but he also knows she’d be slower and would make more mistakes. So she’ll be lucky if she operates more than 15 minutes during the four-hour surgery. And she knows that if she slips up, he’ll tap a touch screen and resume control, very publicly banishing her to watch from the sidelines.

近六个月后，又是在早上六点半，克里斯汀又用轮椅将另一名病人推进手术室，但这一次做前列腺手术的是机器人。主治医师负责设置1000磅重的机器人，将四个机器臂架在病人身上。然后他和克里斯汀回到15英尺之外的操作台上，背朝病人。主治医生远程控制机器臂灵巧地伸缩，并解剖组织，克里斯汀从旁观察。利用机器人，主治医生一个人就能完成手术，事实也基本如此。他知道克里斯汀需要练习，但他也很清楚，她手术的速度更慢，而且更易出错。因此，时长4小时的手术中，她如能亲手操刀15分钟就很幸运了。如果她失手，他可以轻点触屏恢复控制，公然将她“驱逐出场”。

Surgery may be extreme work, but until recently surgeons in training learned their profession the same way most of us learned how to do our jobs: We watched an expert, got involved in the easier work first, and then progressed to harder, often riskier tasks under close supervision until we became experts ourselves. This process goes by lots of names: apprenticeship, mentorship, on-the-job learning (OJL). In surgery it’s called See one, do one, teach one.

外科手术可能是个极端的例子，但此前实习外科医生和其他人掌握工作技能的方法别无二致。我们观摩专家，先参与较简单的工作，然后在严格监督下进入难度更高，且往往风险更大的环节，直到我们自己成为专家。这一过程有多种称谓：学徒期、导师制、在岗学习（OJL）。在外科中的行话是：看一、做一、教一（See one, do one,teach one.）。

Critical as it is, companies tend to take on-the-job learning for granted; it’s almost never formally funded or managed, and little of the estimated $366 billion companies spent globally on formal training in 2018 directly addressed it. Yet decades of research show that although employer-provided training is important, the lion’s share of the skills needed to reliably perform a specific job can be learned only by doing it. Most organizations depend heavily on OJL: A 2011 Accenture survey, the most recent of its kind and scale, revealed that only one in five workers had learned any new job skills through formal training in the previous five years.

尽管在岗学习至关重要，公司却往往对其熟视无睹。很少有公司对在岗学习进行正式的管理或投入，2018年全球公司在正式培训上投入约3660亿美元，然而鲜有资金用于在岗学习。数十年来的研究显示，尽管雇主提供的培训十分重要，但要想真正掌握特定职能所需的技能，必须靠亲身实践。大多组织严重依赖在岗学习。2011年埃森哲进行了迄今规模最大的在岗学习调查，发现只有20%的员工在过去5年里通过正式培训获得了新工作技能。

Today OJL is under threat. The headlong introduction of sophisticated analytics, AI, and robotics into many aspects of work is fundamentally disrupting this time-honored and effective approach. Tens of thousands of people will lose or gain jobs every year as those technologies automate work, and hundreds of millions will have to learn new skills and ways of working. Yet broad evidence demonstrates that companies’ deployment of intelligent machines often blocks this critical learning pathway: My colleagues and I have found that it moves trainees away from learning opportunities and experts away from the action, and overloads both with a mandate to master old and new methods simultaneously.

如今在岗学习面临挑战。复杂分析技术、人工智能和机器人突然闯入了职场的方方面面，从根本上颠覆了这一由来已久的有效学习方式。随着技术让工作越来越自动化，每年都有数以万计的人离职或就业，数以亿计的人必须学习新技能和新工作方式。但更广泛的证据表明，公司部署智能机器会阻碍这一关键的学习渠道：我和我的同事发现，人工智能会让新手失去学习机会，让老手减少实践机会，迫使两者必须同时掌握新方法和旧方法，令他们不堪重负。

John W. Tomac

How, then, will employees learn to work alongside these machines? Early indications come from observing learners engaged in norm-challenging practices that are pursued out of the limelight and tolerated for the results they produce. I call this widespread and informal process shadow learning.

那么，员工能否学会和这些机器共事呢？此前的一些观察来自参与挑战常规实践的学习者，这些实践并非重点，而且人们对其结果的容忍度高。我将这一广泛存在且非正式的流程称为“暗中学习”。

Obstacles to Learning

学习的障碍

My discovery of shadow learning came from two years of watching surgeons and surgical residents at 18 top-rated teaching hospitals in the United States. I studied learning and training in two settings: traditional (“open”) surgery and robotic surgery. I gathered data on the challenges robotic surgery presented to senior surgeons, residents, nurses, and scrub technicians (who prep patients, help glove and gown surgeons, pass instruments, and so on), focusing particularly on the few residents who found new, rule-breaking ways to learn. Although this research concentrated on surgery, my broader purpose was to identify learning and training dynamics that would show up in many kinds of work with intelligent machines.

我在美国18家顶级教学医院中，对外科医生和外科住院医生进行了为期两年的观察，从中发现了暗中学习。我研究了两种情景下的学习和训练：传统（“开放式”）手术和机器人手术。我收集了机器人手术为资深外科医生、住院医生、护士和手术技术员（为患者做术前准备，帮助外科医生戴手套和穿手术服，传递手术器械等）所带来挑战的相关数据，重点关注那些发现了突破传统学习方法的少数住院医生。虽然研究重点是手术，但我更宏大的目的在于：发现可能会在与智能机器共事时出现的学习和培训动态。

To this end, I connected with a small but growing group of field researchers who are studying how people work with smart machines in settings such as internet start-ups, policing organizations, investment banking, and online education. Their work reveals dynamics like those I observed in surgical training. Drawing on their disparate lines of research, I’ve identified four widespread obstacles to acquiring needed skills. Those obstacles drive shadow learning.

为此我与少数实地研究人员建立了联系，他们的成员在不断增加。他们正在研究人们如何在互联网初创企业、警务组织、投资银行和在线教育等场景中使用智能机器。他们的发现与我在外科手术培训中观察到的现象相似。根据他们的几类不同研究，我发现了获取所需技能的四大普遍障碍。这些障碍触发了暗中学习。

1. Trainees are being moved away from their “learning edge.”

1.新手正在失去“学习优势”。

Training people in any kind of work can incur costs and decrease quality, because novices move slowly and make mistakes. As organizations introduce intelligent machines, they often manage this by reducing trainees’ participation in the risky and complex portions of the work, as Kristen found. Thus trainees are being kept from situations in which they struggle near the boundaries of their capabilities and recover from mistakes with limited help—a requirement for learning new skills.

在任何工作中，培训员工都会产生成本并降低质量，因为新手行动缓慢且易犯错。正如克里斯汀所发现的那样，组织迎来智能机器，通常会让受培训者减少参与风险和复杂度高的部分，以此作为管理之策。因此，受培训者将无法获得扩充能力范围边界，并在有限帮助下从错误中成长的机会——而这些恰恰是学习新技能的必要条件。

The same phenomenon can be seen in investment banking New York University’s Callen Anthony found that junior analysts in one firm were increasingly being separated from senior partners as those partners interpreted algorithm-assisted company valuations in M&As. The junior analysts were tasked with simply pulling raw reports from systems that scraped the web for financial data on companies of interest and passing them to the senior partners for analysis.

投资银行里也有同样现象。纽约大学的卡伦·安东尼（Callen Anthony）在某投行中发现，合伙人用算法来协助公司并购并解读估值，使得初级分析师与高级合伙人越离越远。初级分析师的任务仅是从系统中提取原始报告（在网络上对感兴趣公司的财务数据进行收集），然后将其传递给高级合伙人进行分析。

The implicit rationale for this division of labor? First, reduce the risk that junior people would make mistakes in doing sophisticated work close to the customer; and second, maximize senior partners’ efficiency: The less time they needed to explain the work to junior staffers, the more they could focus on their higher-level analysis. This provided some short-term gains in efficiency, but it moved junior analysts away from challenging, complex work, making it harder for them to learn the entire valuation process and diminishing the firm’s future capability.

这种分工的隐含逻辑是什么？首先，降低初级员工在面向客户的复杂工作中犯错的风险；第二，最大化高级合伙人的效率：向初级员工解释工作的时间越少，他们就越能专注于更高级别的分析。这样做短期内效率有所提高，但却剥夺了初级分析师挑战复杂工作的机会，使他们更难以了解整个估值过程，并削弱了公司未来的能力。

2. Experts are being distanced from the work.

2.专家与工作疏远了。

Sometimes intelligent machines get between trainees and the job, and other times they’re deployed in a way that prevents experts from doing important hands-on work. In robotic surgery, surgeons don’t see the patient’s body or the robot for most of the procedure, so they can’t directly assess and manage critical parts of it. For example, in traditional surgery, the surgeon would be acutely aware of how devices and instruments impinged on the patient’s body and would adjust accordingly; but in robotic surgery, if a robot’s arm hits a patient’s head or a scrub is about to swap a robotic instrument, the surgeon won’t know unless someone tells her. This has two learning implications: Surgeons can’t practice the skills needed to make holistic sense of the work on their own, and they must build new skills related to making sense of the work through others.

有时，智能机器会夹在受培训者和工作之间，有时则妨碍专家进行重要实践工作。机器人操作的手术中，外科医生在手术过程的大多数时间都看不到患者的身体或机器人，因此无法直接评估和管理关键环节。例如，在传统手术中，外科医生会敏锐地意识到装置和器械如何碰触患者的身体并进行相应调整。但是在机器人手术中，如果机器臂撞到病人的头部，或者清洁臂即将替换器械，外科医生必须依靠他人提醒才能知道。这对学习有两重影响：外科医生无法磨练全面了解自己工作所需的技能，以及必须通过他人才能获得此类新技能。

Benjamin Shestakofsky, now at the University of Pennsylvania, described a similar phenomenon at a pre-IPO start-up that used machine learning to match local laborers with jobs and that provided a platform for laborers and those hiring them to negotiate terms. At first the algorithms weren’t making good matches, so managers in San Francisco hired people in the Philippines to manually create each match. And when laborers had difficulty with the platform—for instance, in using it to issue price quotes to those hiring, or to structure payments—the start-up managers outsourced the needed support to yet another distributed group of employees, in Las Vegas. Given their limited resources, the managers threw bodies at these problems to buy time while they sought the money and additional engineers needed to perfect the product. Delegation allowed the managers and engineers to focus on business development and writing code, but it deprived them of critical learning opportunities: It separated them from direct, regular input from customers—the laborers and the hiring contractors—about the problems they were experiencing and the features they wanted.

目前供职于宾夕法尼亚大学的本杰明·肖斯塔科夫斯基（Benjamin Shestakofsky）介绍了某一还未上市初创公司中的类似现象。公司使用机器学习来匹配劳动者和职位，为劳动者和雇主提供了商讨条款的平台。起初算法匹配不准，因此旧金山的经理在菲律宾雇人来手动调整每一配对。当劳动者在平台上遇到困难时——例如利用平台向招聘人员报价或发起付款时，初创公司的经理将所需支持外包给拉斯维加斯的另一批员工。由于资源有限，管理人员在这些问题上增加人手争取时间，同时寻找额外的资金和工程师来完善产品。外包让管理者和工程师专注于业务拓展和编程，但却剥夺了他们的关键学习机会，让他们无法获得客户（劳动者和雇佣承包商）的直接定期反馈，比如他们正在经历哪些问题和他们想要的功能。

3. Learners are expected to master both old and new methods.

3.学习者必须掌握新旧两种方法。

Robotic surgery comprises a radically new set of techniques and technologies for accomplishing the same ends that traditional surgery seeks to achieve. Promising greater precision and ergonomics, it was simply added to the curriculum, and residents were expected to learn robotic as well as open approaches. But the curriculum didn’t include enough time to learn both thoroughly, which often led to a worst-case outcome: The residents mastered neither. I call this problem methodological overload.

机器人手术用一套全新的技巧和技术来实现传统手术试图达到的效果。它保证更高的精确度和更优人体工程学，直接被纳入了课程中，住院医生被要求学习机器人知识和传统方法。但课程没有足够的时间让他们两者兼通，这往往会导致最坏的结果：哪种都没有掌握。我将这一难题称为方法超载（methodological overload）。

Shreeharsh Kelkar, at UC Berkeley, found that something similar happened to many professors who were using a new technology platform called edX to develop massive open online courses (MOOCs). EdX provided them with a suite of course-design tools and instructional advice based on fine-grained algorithmic analysis of students’ interaction with the platform (clicks, posts, pauses in video replay, and so on). Those who wanted to develop and improve online courses had to learn a host of new skills—how to navigate the edX user interface, interpret analytics on learner behavior, compose and manage the course’s project team, and more—while keeping “old school” skills sharp for teaching their traditional classes. Dealing with this tension was difficult for everyone, especially because the approaches were in constant flux: New tools, metrics, and expectations arrived almost daily, and instructors had to quickly assess and master them. The only people who handled both old and new methods well were those who were already technically sophisticated and had significant organizational resources.

加州大学伯克利分校的施里哈什·克尔卡（Shreeharsh Kelkar）发现，许多教授正在使用一种名为edX的新技术平台来开发大规模开放式在线课程（MOOCs）。 EdX根据对学生与平台互动（点击、帖子和视频播放中的暂停等）的细粒度算法分析，为学生提供了一套课程设计工具和教学建议。想开发和改进在线课程的人必须学习一系列新技能：如何浏览edX用户界面，解读学习者行为分析数据，组件和管理课程的项目团队，等等，同时还要保留“老派”技能，来讲授传统课程。处理这种矛盾对每个人来说都很困难，况且方法还在不断变化，新的工具、指标和期望几乎每天都出现，教师必须快速评估和掌握它们。唯一能够很好地处理新旧方法的人，是那些已经熟悉技术并拥有重要组织资源的人。

4. Standard learning methods are presumed to be effective.

4.标准学习方法被默认为有效。

Decades of research and tradition hold trainees in medicine to the See one, do one, teach one method, but as we’ve seen, it doesn’t adapt well to robotic surgery. Nonetheless, pressure to rely on approved learning methods is so strong that deviation is rare: Surgical-training research, standard routines, policy, and senior surgeons all continue to emphasize traditional approaches to learning, even though the method clearly needs updating for robotic surgery.

几十年的研究和传统让实习医生遵循“看一、做一、教一”的方法。但如我们所见，它不适应机器人手术。尽管如此，依赖老派学习方法的压力非常大，“离经叛道”者寥寥：外科培训研究、标准程序、政策和高级外科医生都继续强调传统的学习方法，哪怕该方法显然已不适用于机器人手术。

Sarah Brayne, at the University of Texas, found a similar mismatch between learning methods and needs among police chiefs and officers in Los Angeles as they tried to apply traditional policing approaches to beat assignments generated by an algorithm. Although the efficacy of such “predictive policing” is unclear, and its ethics are controversial, dozens of police forces are becoming deeply reliant on it. The LAPD’s PredPol system breaks the city up into 500-foot squares, or “boxes,” assigns a crime probability to each one, and directs officers to those boxes accordingly. Brayne found that it wasn’t always obvious to the officers—or to the police chiefs—when and how the former should follow their AI-driven assignments. In policing, the traditional and respected model for acquiring new techniques has been to combine a little formal instruction with lots of old-fashioned learning on the beat.

类似的，得克萨斯大学的莎拉·布雷恩（Sarah Brayne）发现洛杉矶警长和警官的学习方法和需求之间也存在不匹配。他们试图将传统警务方法应用于算法产生的巡逻任务。尽管这种“预测性警务”的效果尚不清晰，而且在道德上存在争议，但数十支警队越来越依赖它。洛杉矶警察局的PredPol系统将城市划分成500英尺的方格，即“箱子”，计算每个箱子的犯罪概率，将警察分派到每个方格中。布雷恩发现，警官或警长有时并不明白何时以及如何遵循人工智能驱动的任务。在警务方面，传统且受尊重的获取新技术的模式，是将少部分正式课程与大部分传统学习有机结合起来。

Many chiefs therefore presumed that officers would mostly learn how to incorporate crime forecasts on the job. This dependence on traditional OJL contributed to confusion and resistance to the tool and its guidance. Chiefs didn’t want to tell officers what to do once “in the box,” because they wanted them to rely on their experiential knowledge and discretion. Nor did they want to irritate the officers by overtly reducing their autonomy and coming across as micromanagers. But by relying on the traditional OJL approach, they inadvertently sabotaged learning: Many officers never understood how to use PredPol or its potential benefits, so they wholly dismissed it—yet they were still held accountable for following its assignments. This wasted time, decreased trust, and led to miscommunication and faulty data entry—all of which undermined their policing.

因此，许多警长认为，警官们基本能够在工作中运用犯罪预测。对传统OJL的依赖导致了对人工智能工具及其指导的困惑和抵制。局长不想告诉警官一旦被分配到方格里需要做什么，希望他们依靠经验知识和自己的判断行事。局长也不想减少他们的裁量权，被当作微观管理者，从而激怒他们。但是，依靠传统的OJL方法，他们无意中破坏了学习：许多警官从未弄清如何使用PredPol或其潜在的好处，因此对其完全无视。然而他们仍然对智能分配的任务负责。这浪费了时间，减少了信任，导致沟通误会和数据输入错误——所有这些都给警务活动造成了恶劣影响。

Shadow Learning Responses

暗中学习的回应

Faced with such barriers, shadow learners are bending or breaking the rules out of view to get the instruction and experience they need. We shouldn’t be surprised. Close to a hundred years ago, the sociologist Robert Merton showed that when legitimate means are no longer effective for achieving a valued goal, deviance results. Expertise—perhaps the ultimate occupational goal—is no exception。

面临上述阻碍，暗中学习者悄悄绕过或打破规则来获得所需的指导和经验，自然不足为奇。约100年前，社会学家罗伯特·莫顿（Robert Merton）就发现，当合法手段对达成有价值的目标不再奏效时，就会出现非常手段。对于专业知识（或许是职业的终极目标）也不例外。

Given the barriers I’ve described, we should expect people to find deviant ways to learn key skills. Their approaches are often ingenious and effective, but they can take a personal and an organizational toll: Shadow learners may be punished (for example, by losing practice opportunities and status) or cause waste and even harm. Still, people repeatedly take those risks, because their learning methods work well where approved means fail. It’s almost always a bad idea to uncritically copy these deviant practices, but organizations do need to learn from them.

鉴于我描述的障碍，我们应理解人们会采取其他方式学习关键技能。这些方式一般灵活有效，却往往会让个人和组织付出代价：暗中学习者可能会受到惩罚，例如失去实践机会或地位或造成浪费甚至构成伤害。但人们依然一再铤而走险，因为当合规的方式失败时，他们的学习方法奏效。不加鉴别地效仿这些非常手段自然不对，但它们确实有组织值得学习之处。

Following are the shadow learning practices that I and others have observed:

以下是我和他人观察到的一些暗中学习实践。

Seeking struggle.

寻找难点。

Recall that robotic surgical trainees often have little time on task. Shadow learners get around this by looking for opportunities to operate near the edge of their capability and with limited supervision. They know they must struggle to learn, and that many attending physicians are unlikely to let them. The subset of residents I studied who did become expert found ways to get the time on the robots they needed. One strategy was to seek collaboration with attendings who weren’t themselves seasoned experts. Residents in urology—the specialty having by far the most experience with robots—would rotate into departments whose attendings were less proficient in robotic surgery, allowing the residents to leverage the halo effect of their elite (if limited) training. The attendings were less able to detect quality deviations in their robotic surgical work and knew that the urology residents were being trained by true experts in the practice; thus they were more inclined to let the residents operate, and even to ask for their advice. But few would argue that this is an optimal learning approach.

让我们回顾一下在机器人手术中没有足够时间学习的外科实习生。暗中学习者通过寻求接近他们能力极限，且受到很少监督的机会来达到目的。他们知道自己在学习中一定会遇到难点，而且很多主治医生不太会给他们学习的机会。我研究的一小群成为专家的实习医生设法找机会操作机器人。一大策略是，与那些本身经验不足的主治医生寻求合作。目前泌尿科的实习医生在机器人手术上经验最丰富，他们可以轮岗到那些主治医生不太熟悉机器人手术的科室，尽管自身的培训有限，泌尿科实习医生也可以利用本科室的（在机器人手术上的）光环效应。其他科室的主治医生不太有能力判断他们在机器人手术上的水平差异，会觉得泌尿科实习医生受到过真正的专家培训；因此他们更愿意让泌尿科的实习医生做手术，甚至咨询他们意见。但很少有人认为这是最佳学习之道。

What about those junior analysts who were cut out of complex valuations? The junior and senior members of one group engaged in shadow learning by disregarding the company’s emerging standard practice and working together. Junior analysts continued to pull raw reports to produce the needed input, but they worked alongside senior partners on the analysis that followed.

而那些无法参与负责估值的初级分析师呢？同组的初级和高级成员也会暗中学习——无视公司中出现的标准操作，一起工作。初级分析师继续准备原始报告来生成所需的数据，但也与高级合伙人一同处理随后的分析。

In some ways this sounds like a risky business move. Indeed, it slowed down the process, and because it required the junior analysts to handle a wider range of valuation methods and calculations at a breakneck pace, it introduced mistakes that were difficult to catch. But the junior analysts developed a deeper knowledge of the multiple companies and other stakeholders involved in an M&A and of the relevant industry and learned how to manage the entire valuation process. Rather than function as a cog in a system they didn’t understand, they engaged in work that positioned them to take on more-senior roles. Another benefit was the discovery that, far from being interchangeable, the software packages they’d been using to create inputs for analysis sometimes produced valuations of a given company that were billions of dollars apart. Had the analysts remained siloed, that might never have come to light.

从某种程度说，这种举动听起来风险颇高，事实上也确实拖延了进程。而且初级分析师由于被要求飞速处理多种估值方法和计算，所以会导致很难被发现的错误。但因为初级分析师从中获得了多家公司、相关行业和其他参与并购利益相关方的更深刻知识，以及学会如何管理完整估值流程，他们不再仅仅是自己一无所知的系统中的一颗螺丝钉，而是能参与更高一级的工作。另一个有益发现是，他们用来生成输入分析数据的软件包不仅不能互相替换，甚至在进行同一家公司的估值时会出现几十亿美元的差距。要不是分析师们走出了孤岛，可能永远意识不到这点。

Tapping frontline know-how.

利用前线知识。

As discussed, robotic surgeons are isolated from the patient and so lack a holistic sense of the work, making it harder for residents to gain the skills they need. To understand the bigger picture, residents sometimes turn to scrub techs, who see the procedure in its totality: the patient’s entire body; the position and movement of the robot’s arms; the activities of the anesthesiologist, the nurse, and others around the patient; and all the instruments and supplies from start to finish. The best scrubs have paid careful attention during thousands of procedures. When residents shift from the console to the bedside, therefore, some bypass the attending and go straight to these “superscrubs” with technical questions, such as whether the intra-abdominal pressure is unusual, or when to clear the field of fluid or of smoke from cauterization. They do this despite norms and often unbeknownst to the attending.

如前所述，参与机器人手术的外科医生与病人隔离，因此缺乏对工作的整体认知，让实习医生更难掌握所需的技巧。为了解全局，实习医生有时需要求助于全程跟进手术的技术员：他/她能看到病人全身、机器臂的位置和移动、麻醉师、护士及其他病人旁边的医生的活动，以及所有的器械和用品。最优秀的手术技术员仔细观摩了几千场手术。当实习医生从控制台走到患者床边时，有些人甚至绕过主治医生，直接向这些“超级技师”提问，比如腹腔压正常与否，或者什么时候清理创面液体或灼烧产生的烟雾。他们这样做不合常规，而且往往主治医生并不知情。

And what about the start-up managers who were outsourcing jobs to workers in the Philippines and Las Vegas? They were expected to remain laser focused on raising capital and hiring engineers. But a few spent time with the frontline contract workers to learn how and why they made the matches they did. This led to insights that helped the company refine its processes for acquiring and cleaning data—an essential step in creating a stable platform. Similarly, some attentive managers spent time with the customer service reps in Las Vegas as they helped workers contend with the system. These “ride alongs” led the managers to divert some resources to improving the user interface, helping to sustain the start-up as it continued to acquire new users and recruit engineers who could build the robust machine learning systems it needed to succeed.

那么将工作外包到菲律宾和拉斯维加斯的初创公司经理呢？他们理应聚焦于筹集资金和聘用工程师。但有几名经理与前线合同工进行了接触，学习了他们做出搭配的过程和原因，从而获得了帮助公司改善捕捉和过滤数据的流程——让平台稳定运转的关键步骤。类似的，一些有心的经理花时间了解了拉斯维加斯的客服代表如何提高求职者满意度。这些“顺便一做”的事情让经理能拿出部分资源改善用户界面。因此，随着新用户增加，初创公司得以延续，并能聘用工程师让机器学习系统更稳健，为公司成功夯实基础。

Redesigning roles.

重新设计角色。

The new work methods we create to deploy intelligent machines are driving a variety of shadow learning tactics that restructure work or alter how performance is measured and rewarded. A surgical resident may decide early on that she isn’t going to do robotic surgery as a senior physician and will therefore consciously minimize her robotic rotation. Some nurses I studied prefer the technical troubleshooting involved in robotic assignments, so they surreptitiously avoid open surgical work. Nurses who staff surgical procedures notice emerging preferences and skills and work around blanket staffing policies to accommodate them. People tacitly recognize and develop new roles that are better aligned with the work—whether or not the organization formally does so.

我们创造利用智能机器的新工作方法，推动了多种暗中学习策略，改变了工作逻辑或绩效考核及奖励方法。一名外科住院医生可能很早就会决定未来成为高级医师后不会涉猎机器人手术，因此有意尽可能减少机器人手术轮岗。我研究中的一些护士偏好在机器人任务中解决技术难题，因此他们尽量暗中避开传统手术工作。负责配备外科手术人手的护士注意到了偏好和技术苗头，设法绕开人力政策进行协调。人们悄悄地意识到并设置新角色，以便更好地适应工作——无论此前组织是否有正式规定。

Consider how some police chiefs reframed expectations for beat cops who were having trouble integrating predictive analytics into their work. Brayne found that many officers assigned to patrol PredPol’s “boxes” appeared to be less productive on traditional measures such as number of arrests, citations, and FIs (field interview cards—records made by officers of their contacts with citizens, typically people who seem suspicious). FIs are particularly important in AI-assisted policing, because they provide crucial input data for predictive systems even when no arrests result. When cops went where the system directed them, they often made no arrests, wrote no tickets, and created no FIs.

对那些有困难将预测性分析技术与工作结合的巡警，警长也会重新考量对他们的期待。布雷恩发现，很多被分配到“方框”里的警官按照逮捕、口供、FI（面谈记录卡，即警官调查可疑人员的记录）等传统指标考核似乎效率欠佳。FI通常在人工智能辅助警务中非常重要，因为即使没有逮捕结果，它们也为预测系统提供了关键输入数据。警察到达系统提示的地点，往往不会进行逮捕，也不会开具传票，不写FI。

Recognizing that these traditional measures discouraged beat cops from following PredPol’s recommendations, a few chiefs sidestepped standard practice and publicly and privately praised officers not for making arrests and delivering citations but for learning to work with the algorithmic assignments. As one captain said, “Good, fine, but we are telling you where the probability of a crime is at, so sit there, and if you come in with a zero [no crimes], that is a success.” These chiefs were taking a risk by encouraging what many saw as bad policing, but in doing so they were helping to move the law enforcement culture toward a future in which the police will increasingly collaborate with intelligent machines, whether or not PredPol remains in the tool kit.

意识到这些传统方法会导致巡警不理会PredPol的推荐，一些警长绕开标准做法，并且在公开和私下对没有达成逮捕和口供结果、但完成了智能出警任务的警官进行表扬。正如一位副巡长所说：“好的，没问题，我们只是告诉你这里发生犯罪的概率是多少，所以在那儿待一会儿，哪怕空手而归也算成功。”这些警长担着风险，鼓励被很多人视为收效欠佳的出警。但他们这样做能促进执法向着与智能机器紧密合作的未来迈进，无论PredPol那时是否存在。

Curating solutions.

设计解决方案。

Trainees in robotic surgery occasionally took time away from their formal responsibilities to create, annotate, and share play-by-play recordings of expert procedures. In addition to providing a resource for themselves and others, making the recordings helped them learn, because they had to classify phases of the work, techniques, types of failures, and responses to surprises.

机器人手术的实习生偶尔会抽出时间，制作、注释并分享带解说的专业手术录像，这类工作并不属于他们的职责范畴。录像不仅能给自己或别人提供资源，还能帮助他们学习，因为他们必须将工作、技术、失败类型以及对意外的回应进行阶段划分。

Faculty members who were struggling to build online courses while maintaining their old-school skills used similar techniques to master the new technology. EdX provided tools, templates, and training materials to make things easier for instructors, but that wasn’t enough. Especially in the beginning, far-flung instructors in resource-strapped institutions took time to experiment with the platform, make notes and videos on their failures and successes, and share them informally with one another online. Establishing these connections was hard, especially when the instructors’ institutions were ambivalent about putting content and pedagogy online in the first place.

在设计在线课程中遇到困难，同时还需要保持传统教学技能的教师，也利用类似的技巧来掌握新技术。EdX提供工具、模板和培训材料，降低教师学习的难度，但还不够。尤其是一开始，分散在各地、资源紧缺的教师花时间实验平台，把自己的成败经验做成笔记或视频，然后在网上与其他人分享。建立这种联系很难，更不要说教师所在的院校对在线教育抱持矛盾态度。

Shadow learning of a different type occurred among the original users of edX—well-funded, well-supported professors at topflight institutions who had provided early input during the development of the platform. To get the support and resources they needed from edX, they surreptitiously shared techniques for pitching desired changes in the platform, securing funding and staff support, and so on.

EdX初始用户的暗中学习还有另外一种方式——来自资金和资源充足的一流院校教授，他们在平台开发期间提供了早期投入。为了从edX获得所需的支持和资源，他们私下分享技巧，来促成平台上的理想变化，并保证资金和人员支持，等等。

Learning from shadow learners.

从暗中学习者身上吸取经验。

Obviously shadow learning is not the ideal solution to the problems it addresses. No one should have to risk getting fired just to master a job. But these practices are hard-won, tested paths in a world where acquiring expertise is becoming more difficult and more important.

显然，暗中学习并非是问题的理想解决之道。没有人愿意冒着被解雇的风险来掌握一门技艺。但这些实践来之不易，也是在获得专业知识越来越难、越来越重要情况下的重要探索。

The four classes of behavior shadow learners exhibit—seeking struggle, tapping frontline know-how, redesigning roles, and curating solutions—suggest corresponding tactical responses. To take advantage of the lessons shadow learners offer, technologists, managers, experts, and workers themselves should:

以上四种暗中学习者的行为——寻找难点，利用前线知识，重新设计角色和设计解决方案，分别对应了不同的策略。为了利用这些暗中学习者的经验，技术专家、经理、专家和员工自身应该做到：

● ensure that learners get opportunities to struggle near the edge of their capacity in real (not simulated) work so that they can make and recover from mistakes

  保证学习者有机会在实际工作而非模拟任务中，触及他们能力的边界。因此他们能够犯错并改错。

● foster clear channels through which the best frontline workers can serve as instructors and coaches

   创造清晰的渠道，让最优秀的前线员工能成为导师。

● restructure roles and incentives to help learners master new ways of working with intelligent machines

   重新调整职位结构和奖励机制，帮助学习者掌握与智能机器共事的新方法。

● build searchable, annotated, crowdsourced “skill repositories” containing tools and expert guidance that learners can tap and contribute to as needed

   建立可搜索、有注释、众包的“技能储备库”，其中配备学习者能够利用并贡献的必须工具和专业指导。

The specific approach to these activities depends on organizational structure, culture, resources, technological options, existing skills, and, of course, the nature of the work itself. No single best practice will apply in all circumstances. But a large body of managerial literature explores each of these, and outside consulting is readily available.

这些活动具体的方法取决于组织结构、文化、资源、技术选择、现有技术，以及工作本身的性质。没有一套放之四海而皆准的最佳实践。但很多管理文献对上述各方面都有探索，而且也有外部咨询可供选择。

More broadly, my research, and that of my colleagues, suggests three organizational strategies that may help leverage shadow learning’s lessons:

更广泛地，我和我同事的研究提供了三条组织战略建议，有助于利用暗中学习的经验。

1. Keep studying it.

1.持续学习。

Shadow learning is evolving rapidly as intelligent technologies become more capable. New forms will emerge over time, offering new lessons. A cautious approach is critical. Shadow learners often realize that their practices are deviant and that they could be penalized for pursuing them. (Imagine if a surgical resident made it known that he sought out the least-skilled attendings to work with.) And middle managers often turn a blind eye to these practices because of the results they produce—as long as the shadow learning isn’t openly acknowledged. Thus learners and their managers may be less than forthcoming when an observer, particularly a senior manager, declares that he wants to study how employees are breaking the rules to build skills. A good solution is to bring in a neutral third party who can ensure strict anonymity while comparing practices across diverse cases. My informants came to know and trust me, and they were aware that I was observing work in numerous work groups and facilities, so they felt confident that their identities would be protected. That proved essential in getting them to open up.

随着智能技术变得更强大，暗中学习也在迅速发展。新形式将随着时间的推移而出现，提供新的经验。保持谨慎至关重要。暗中学习者经常意识到他们的做法不符合常规，并且他们可能因为自己的做法而受到惩罚。（试想如果一位外科住院医生让别人知道他/她想找最不熟练的主治医师合作。）因为能产生效果，只要暗中学习者不公开承认，中层管理者经常对这些做法视而不见。当观察者，特别是高级管理者宣布想研究员工如何靠违反规则来获得技能时，学习者及其管理者可能不愿意分享经验。比较好的解决方案是，引入中立的第三方，可以确保严格的匿名性，同时比较不同案例的做法。我的线人开始了解并信任我，他们意识到我在许多工作组和设施中观察工作，因此他们确信自己的身份会受到保护。这对于让他们说出真相至关重要。

2. Adapt the shadow learning practices you find to design organizations, work, and technology.

2.调整你发现的暗中学习实践来适应构建组织、工作和技术。

Organizations have often handled intelligent machines in ways that make it easier for a single expert to take more control of the work, reducing dependence on trainees’ help. Robotic surgical systems allow senior surgeons to operate with less assistance, so they do. Investment banking systems allow senior partners to exclude junior analysts from complex valuations, so they do. All stakeholders should insist on organizational, technological, and work designs that improve productivity and enhance on-the-job learning. In the LAPD, for example, this would mean moving beyond changing incentives for beat cops to efforts such as redesigning the PredPol user interface, creating new roles to bridge police officers and software engineers, and establishing a cop-curated repository for annotated best practice use cases.

组织对智能机器的处置往往停留在让个别专家控制工作，减少对受训者依赖的层面。机器人手术系统允许高级外科医生在较少的帮助下操作，他们照做了。投资银行系统允许高级合伙人将初级分析师从复杂的估值工作中排除，他们也照做了。所有利益相关者都应坚持让组织，技术和工作设计提高生产力和加强OJL。例如，在洛杉矶警察局中，这将意味着改变对巡警的激励措施，重新设计PredPol用户界面，创建新角色来连接警察和软件工程师，以及由警察发起建立带注释的最佳实践案例库。

3. Make intelligent machines part of the solution.

3.使智能机器成为解决方案的一部分。

AI can be built to coach learners as they struggle, coach experts on their mentorship, and connect those two groups in smart ways. For example, when Juho Kim was a doctoral student at MIT, he built ToolScape and LectureScape, which allow for crowdsourced annotation of instructional videos and provide clarification and opportunities for practice where many prior users have paused to look for them. He called this learnersourcing. On the hardware side, augmented reality systems are beginning to bring expert instruction and annotation right into the flow of work.

人工智能可以在学习者遇到难题时提供帮助，为作为导师的专家提供培训，并巧妙地连接这两个群体。例如，金柱赫（Juho Kim）在麻省理工学院读博时建立了ToolScape和Lecture-Scape，可以众包方式为教学视频加注释，并为之前暂停寻找注释的用户提供澄清解释和机会。他将之称为学习者采购。在硬件方面，增强现实系统开始将专家指导和注释带入工作流中。

Existing applications use tablets or smart glasses to overlay instructions on work in real time. More-sophisticated intelligent systems are expected soon. Such systems might, for example, superimpose a recording of the best welder in the factory on an apprentice welder’s visual field to show how the job is done, record the apprentice’s attempt to match it, and connect the apprentice to the welder as needed. The growing community of engineers in these domains have mostly been focused on formal training, and the deeper crisis is in on-the-job learning. We need to redirect our efforts there.

现有应用程序使用平板电脑或智能眼镜，将指导实时添加到工作上。预计很快就会有更复杂的智能系统。例如，这样的系统可以在学徒焊工的视野中叠加工厂中模范焊工的录像，显示工作如何完成，记录学徒的尝试与之对比，并根据需要将学徒与模范焊工联系起来。这些领域不断增长的工程师社区大多专注于正式培训，更深层次的危机是OJL。我们需要重新分配在OJL上的精力。

For thousands of years, advances in technology have driven the redesign of work processes, and apprentices have learned necessary new skills from mentors. But as we’ve seen, intelligent machines now motivate us to peel apprentices away from masters, and masters from the work itself, all in the name of productivity. Organizations often unwittingly choose productivity over considered human involvement, and learning on the job is getting harder as a result. Shadow learners are nevertheless finding risky, rule-breaking ways to learn. Organizations that hope to compete in a world filling with increasingly intelligent machines should pay close attention to these “deviants.” Their actions provide insight into how the best work will be done in the future, when experts, apprentices, and intelligent machines work, and learn, together.

几千年来，技术的进步推动了工作流程的重新设计，学徒们从导师那里获得了必要的新技能。但正如我们所见，现在智能机器正以生产率为名，迫使我们让学徒与导师脱离，让导师与工作脱离。组织通常在不经意间选择生产率而非员工参与，因此在工作中学习变得越来越困难。然而，暗中学习者正在寻找有风险、打破常规的学习方法。想在智能机器世界中竞争的组织应该密切关注这些“不按常理出牌的人”。他们的行动可以让你深入了解，当未来专家、学徒和智能机器共同工作和学习时，如何以最佳方式完成工作。

马特·比恩是加州大学圣巴巴拉分校技术管理助理教授，也是麻省理工学院数字经济项目研究成员。