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The relationship between AI and humans

AI与人类

What questions do technologies like ChatGPT raise for employees and customers?

ChatGPT这类技术将会给员工和客户带来什么问题？

If you ask something of ChatGPT, an artificial-intelligence (AI) tool that is all the rage, the responses you get back are almost instantaneous, utterly certain and often wrong. It is a bit like talking to an economist. The questions raised by technologies like ChatGPT yield much more tentative answers. But they are ones that managers ought to start asking.

如果你向现在大热的AI工具ChatGPT提问，它几乎会立刻回答你，并且语气笃定，虽然答案经常是错的。这倒挺像是在和经济学家交谈（一本正经说瞎话）。针对ChatGPT这类AI技术引发的问题，虽然答案还不明确，但管理人员确实应该开始思考这些问题。

One issue is how to deal with employees’ concerns about job security. Worries are natural. An AI that makes it easier to process your expenses is one thing; an AI that people would prefer to sit next to at a dinner party quite another. Being clear about how workers would redirect time and energy that is freed up by an AI helps foster acceptance. So does creating a sense of agency: research conducted by MIT Sloan Management Review and the Boston Consulting Group found that an ability to override an AI makes employees more likely to use it.

一大问题是如何处理员工对工作稳定性的担忧。有担忧很正常。毕竟通过AI让报销更加简便是一回事，但愿意在聚餐时和AI并排坐则是另一回事了 。了解员工如何利用AI省下的时间和精力可以提高（员工）对AI的接受度。激发能动性也能起到这样的效果：《麻省理工斯隆管理评论》和波士顿咨询集团的研究发现，能力比AI强的员工更愿意使用AI。

Whether people really need to understand what is going on inside an AI is less clear. Intuitively, being able to follow an algorithm’s reasoning should trump being unable to. But a piece of research by academics at Harvard University, the Massachusetts Institute of Technology and the Polytechnic University of Milan suggests that too much explanation can be a problem.

但对于是否真的需要人们了解AI的内部逻辑，这个问题的答案就没那么清晰了。乍一看能明白（AI的）算法推理自然比无法理解要好。但一项来自哈佛大学、麻省理工学院和米兰理工大学的研究表明，解释太多反而会带来问题。

Employees at Tapestry, a portfolio of luxury brands, were given access to a forecasting model that told them how to allocate stock to stores. Some used a model whose logic could be interpreted; others used a model that was more of a black box. Workers turned out to be likelier to overrule models they could understand because they were, mistakenly, sure of their own intuitions. Workers were willing to accept the decisions of a model they could not fathom, however, because of their confidence in the expertise of people who had built it. The credentials of those behind an ai matter.

奢侈品企业集团泰佩思琦（Tapestry）让员工使用一款配货预测模型。一些员工使用的是逻辑可理解的模型，其他员工则使用一款逻辑不可理解的模型。结果（发现）员工更可能去否决逻辑可理解模型做出的判断，因为他们确信自己的直觉是对的（事实上却是错的）。然而，若员工无法理解模型的逻辑，他们则更愿意采纳模型的决策，因为他们相信模型开发人员的专业能力。（因此）AI背后的团队的资质很重要。

The different ways that people respond to humans and to algorithms is a burgeoning area of research. In a recent paper Gizem Yalcin of the University of Texas at Austin and her co-authors looked at whether consumers responded differently to decisions—to approve someone for a loan, for example, or a country-club membership—when they were made by a machine or a person. They found that people reacted the same when they were being rejected. But they felt less positively about an organisation when they were approved by an algorithm rather than a human. The reason? People are good at explaining away unfavourable decisions, whoever makes them. It is harder for them to attribute a successful application to their own charming, delightful selves when assessed by a machine. People want to feel special, not reduced to a data point.

人类对真人和算法做出的决定的不同反应是一个热门的研究领域。美国得克萨斯大学奥斯汀分校的吉泽姆·亚尔辛（ Gizem Yalcin）与其合著者最近发表了一篇论文，探究面对机器或人工做出的决策（比方说批准贷款或是审核乡村俱乐部的入会资格），消费者是否会有不同反应。亚尔辛等人发现被拒时人们的反应别无二致，不过，如果审批通过的一方是机器而不是人工，人们对这个机构的好感度会下降。为什么呢？因为面对不利的决策，无论是谁做出的，人们都擅长为之开脱。但当有利于自己的决策是由机器做出时，人们把申请成功与个人魅力突出和性格讨喜联系起来会相对困难。人人都希望感受到自己的与众不同，而不仅仅是一个（由算法得出的）分数。

In a forthcoming paper, meanwhile, Arthur Jago of the University of Washington and Glenn Carroll of the Stanford Graduate School of Business investigate how willing people are to give rather than earn credit—specifically for work that someone did not do on their own. They showed volunteers something attributed to a specific person—an artwork, say, or a business plan—and then revealed that it had been created either with the help of an algorithm or with the help of human assistants. Everyone gave less credit to producers when they were told they had been helped, but this effect was more pronounced for work that involved human assistants. Not only did the participants see the job of overseeing the algorithm as more demanding than supervising humans, but they did not feel it was as fair for someone to take credit for the work of other people.

与此同时，华盛顿大学的亚瑟·贾戈（Arthur Jago）与斯坦福大学商学院的格伦•卡罗尔（Glenn Carroll）在一篇即将发表的论文中研究了人们去认可别人而非被别人认可的意愿，尤其是针对那些并非以一人之力完成的工作。他们向志愿者展示了归在某人名下的作品，比如一件艺术品或是一份商业计划，然后告诉志愿者这些作品其实是在算法协助或人工协助下诞生的。当得知创作者是在协助下完成时，每个人对其认可程度都打了折扣，而这一影响在人工协助的一组中更为显著。参加实验的志愿者不仅认为监督算法比监督人更难，还认为创作者把别人的劳动汗水归功在自己头上并不公平。

注释：

1. 原文所提论文的abstract：Producers and creators often receive assistance with work from other people. Increasingly, algorithms can provide similar assistance. When algorithms assist or augment producers, does this change individuals' willingness to assign credit to those producers? Across four studies spanning several domains (e.g., painting, construction, sports analytics, and entrepreneurship), we find evidence that producers receive more credit for work when they are assisted by algorithms, compared with humans. We also find that individuals assume algorithmic assistance requires more producer oversight than human assistance does, a mechanism that explains these higher attributions of credit (Studies 1-3). The greater credit individuals assign to producers assisted by algorithms (vs. other people) also manifests itself in increased support for those producers' entrepreneurial endeavors (Study 4). As algorithms proliferate, norms of credit and authorship are likely changing, precipitating a variety of economic and social consequences.

2. take credit for sth. : To receive recognition, praise, or approval for something, whether or not it is deserved.

Another paper, by Anuj Kapoor of the Indian Institute of Management Ahmedabad and his co-authors, examines whether ais or humans are more effective at helping people lose weight. The authors looked at the weight loss achieved by subscribers to an Indian mobile app, some of whom used only an AI coach and some of whom used a human coach, too. They found that people who also used a human coach lost more weight, set themselves tougher goals and were more fastidious about logging their activities. But people with a higher body mass index did not do as well with a human coach as those who weighed less. The authors speculate that heavier people might be more embarrassed by interacting with another person.

印度管理学院艾哈迈达巴德分校的阿努杰·普尔（Anuj Kapoor）与其合著者在论文中调查了人工智能和教练哪一个更能有效地帮人减肥。作者们以一款印度软件中用户的减肥成果作为研究主题，其中一些用户只使用AI教练，而另一些也有真人教练指导。他们发现，后者减重更多，并为自己设定了更严格的目标，对记录自己的活动更加一丝不苟。但在真人教练的指导下，体质指数较高的用户表现得不如那些体重较小的用户。作者推测，在与他人交流时，体重较重的人可能更容易感到不好意思。

The picture that emerges from such research is messy. It is also dynamic: just as technologies evolve, so will attitudes. But it is crystal-clear on one thing. The impact of ChatGPT and other ais will depend not just on what they can do, but also on how they make people feel.

这些研究所呈现的结果还没有头绪，但同时（这些结果）也是动态的。正如随着技术的进步，人们对技术的态度也在变化。但有一点是十分明晰的：ChatGPT与其他AI所产生的影响不仅取决于它们能做到什么，还取决于它们能让人类感受到什么。

翻译组：

Charlie，往者不谏，来者可追

YY，愿逆风如解意，容易莫摧残

Benjamin，MTI to be，初学翻译的翻译小学生

校对组：

Vince，语言是前进路上的一道光

Jemma，一手抓吃饭，一手抓学习

Rachel，学理工科，爱跳芭蕾，热爱文艺的非典型翻译