MTI外刊阅读精选自由交流Free exchangeUser-rating systems are cut-rate substitutes for a skilful manager用户评分系统只能算优秀管理人才的二流替代品IT OFTEN arrives as you stroll from the kerb to your front door. An e-mail with a question: how many stars do you want to give your Uber driver? Rating systems like the ride-hailing firms are essential infrastructure in the world of digital commerce. Just about anything you might seek to buy online comes with a crowdsourced rating, from a subscription to this newspaper to a broken iPhone on eBay to, increasingly, people providing services. But people are not objects. As ratings are applied to workers it is worth considering the consequencesfor rater and rated.你从马路边慢慢向家门口走去。通常在这个时候，你会收到一封电子邮件，问：为你的优步司机打几颗星?在电子商务的世界中，像这家网约车公司所用的评分系统是必不可少的基础设施。几乎所有你可能从网上购买的东西都带有大众评分，从订阅本刊到 eBay上的一部破 iPhone莫不如此，给提供服务的人打分的情况也越来越多。但人不是物品。当评分系统被用到员工身上时，就有必要思考其后果，包括对评分者和被评分者的后果。User-rating systems were developed in the 1990s. The web held promise as a grand bazaar, where anyone could buy from or sell to anyone else. But e-commerce platforms had to create trust. Buyers and sellers needed to believe that payment would be forthcoming, and that the product would be as described. E-tailers like Amazon and eBay adopted reputation systems, in which sellers and buyers gave feedback about transactions. Reputation scores appended to products, vendors and buyers gave users confidence that they were not about to be scammed.用户评分系统是上世纪 90年代被开发出来的。当时的互联网似乎将成为一个大巴扎，人人都可以在上面买卖东西。但电子商务平台必须建立起信任。卖家得相信自己会收到付款，买家得相信商品不会货不对版。亚马逊和 eBay这样的电子零售商采用了信誉系统，供卖家和买家就交易给出反馈。提供了关于产品、卖家和买家的信誉评分，就可以让用户定下心来，相信自己不会受欺诈。Such systems then spread to labour markets. Workers for gig-economy firms like Uber and Upwork come with user-provided ratings. Conventional employers are jumping on the bandwagon. A phone call to your bank, or the delivery of a meal ordered online, is now likely to be followed by a notification prompting you to rate the person who has just served you.这类系统随后也被运用到劳动力市场中。零工经济公司优步和 Upwork的员工要接受用户的评分。传统雇主也开始跟风。如今不管是给银行打个电话还是接收网上订餐，都有可能事后收到一条推送，请你给刚刚为你提供过服务的人打分。Superficially, such ratings also seem intended to build trust. For users of Uber, say, who will be picked up by drivers they do not know, ratings look like a way to reassure them that their ride will not end in abduction. Yet if that was once necessary, it is no longer. Uber is a global firm worth tens of billions of dollars and with millions of repeat customers. Its customers know by now that the app records drivers identities and tracks their route. It is Ubers brand that creates trust; for most riders, waiting for a driver with a rating of 4.8 rather than 4.5 is not worth the trouble.表面上看，设置这类评分似乎同样是为了建立信任。举例来说，对于并不认识将要前来接送自己的司机的优步用户来说，评分看似是一个让他们放心自己不会不慎上了贼车遭到绑架的方法。不过，假如说这在以前还有必要，如今已不再是这样。优步是一家价值数百亿美元、拥有几百万回头客的全球公司。它的客户如今已经知道应用会记录司机的身份信息并追踪他们的行程。建立起信任的是优步这个品牌。对大多数乘客来说，并不值得费工夫去等待一个评分为 4.8的司机而无视一个 4.5分的司机。Rather, ratings increasingly function to make management cheaper by shifting the burden of monitoring workers to users. Though Uber regards its drivers as independent contractors, in many ways they resemble employees. The firm seeks to provide users with a reasonably uniform experience from ride to ride. And because drivers are randomly assigned to customers, it is the platform that cares whether rides lead to repeat business and which therefore bears the cost of poor behaviour by drivers. Ordinarily a firm in such a position would need to invest heavily in monitoring its workershiring staff to carry out quality assurance by taking Uber rides incognito, for instance. A rating system, however, reduces the need for monitoring by aligning the firms interests with those of workers. (Drivers with low ratings risk having their profile deactivated.)实际上，由于评分系统将监督员工的重负转移到了用户的身上，它正在越来越多地发挥降低管理成本的作用。虽然优步将旗下司机视作独立合同工，但他们在很多方面都与正式员工相似。优步力图让用户每次乘车都能获得大体上一致的服务质量。而由于司机是随机分配给客户的，需要担心每次服务能否带来回头客的就是平台而非司机，司机的不良行为产生的代价因而也是由平台来承担。一般来说，处于这样一种状况的企业需要在监督员工方面有大笔投入，例如雇用评分人员隐瞒身份乘车，从而把控服务品质。然而，评分系统将企业和员工的利益绑在了一起，监督员工的必要性也就降低了。(若评分太低，司机就面临账号被封的风险。)Outsourcing management like this appeals to cost-conscious firms of all sorts; hence the proliferation of technological nudges to rate one service worker or another. To work as intended, however, ratings must provide an accurate indication of how well workers conform to the behaviour that firms desire. Frequently, they do not. Raters may have no incentive to do their job well. They may ignore the prompt to rate a worker, or automatically assign the highest score. They may adhere to social norms that discourage leaving a poor rating, just as diners often leave the standard tip, however unexceptional the service. Ubers customers often award drivers five stars rather than feel bad about themselves for damaging a strangers work prospects. And even when users are accurate, their ratings may reflect factors beyond a service providers control, such as unexpected traffic. Systems that allow users to leave more detailed feedback (as Ubers has begun to) could address this, but at the cost of soaking up more time, which could mean fewer reviews.这种将管理外包出去的做法吸引了各类有意控制成本的公司，所以人们的手机上开始收到各种敦促，要你给这位或那位服务人员评分。然而评分若要发挥预期的功效，必须能精确反映员工在多大程度上遵守了公司的行为准则。但现实中常常不是这样。评分者也许并没有动力好好打分。他们也许会忽略为一位员工打分的提示，或者想也不想就打出最高分。他们可能会受制于社会规范，不便给人打出偏低的分数，就像食客们不管餐厅的服务多一般，往往还是会按常规标准给小费一样。优步乘客不愿意因为破坏一个陌生人的工作前景而有心理负担，因而常常会给司机五星好评。而就算用户给出了恰如其分的评分，所反映的也可能是超出服务提供者控制的因素，例如意想不到的交通拥堵。那些鼓励用户给出更详细反馈的系统(优步已经开始使用)或许能解决这个问题，但代价是占用用户更多时间，因此他们给出的点评可能会减少。When the quality of a match between a worker and a task is particularly important, the problem of sorting the signal from the noise in rating systems grows. Skilled managers can tell when a worker struggling in one role might thrive in another; rating systems can capture only expressions of customer dissatisfaction. Such difficulties also affect gig-economy platforms. Poor ratings on a job-placement site could reflect an inappropriate pairing between a worker with one set of skills and a firm that needs another, rather than the workers failure of effort or ability.员工与任务之间的匹配度特别重要，因而难以从评分系统中找到准确信息的问题也在增加。有经验的管理人员能够看得出，一个难以胜任某个角色的员工也许能从容应对另外一个。而评分系统只能捕捉到客户的不满之声。这类难题对零工经济平台也有影响。如果就业安置网站上的评分不高，与其说反映出员工努力不够或能力欠缺，倒有可能显示平台匹配不当：员工具备某种技能，而企业需要的是另外一种。Platforms can reduce the potential for such errors by including more information about tasks and the workers who might tackle them. Yet they may discover to their chagrin that more information also provides users with more opportunities to discriminate. An analysis of Upwork, for example, found that employers of Indian descent disproportionately sought Indian nationals for their tasks. True, this particular sort of information could be concealedand conventional management permits plenty of discrimination. But firms typically have a legal obligation not to discriminate, and to train managers accordingly.平台可以通过一个办法来减少这类差错：提供更多关于工作任务以及有可能接手这些任务的员工的信息。然而它们也许会懊恼地发现，提供了更多信息，用户也就有了更多差别对待的机会。例如，对 Upwork的一项分析发现，印度裔雇主聘请印度人为自己做事的比例奇高。当然，平台可以将特定信息隐藏起来，而且传统管理下也可能会发生很多歧视现象。但企业通常有反歧视以及对管理人员提供相关培训的法律责任。