Most of us like to make our own choices on matters of life, death and money — and if we cannot decide for ourselves, we would rather turn to a human expert for

我们大多数人喜欢在生死和钱财方面自己做出决定——如果我们无法自己做出决定，我们更愿意向人类专家、而不是冷冰冰的程序求助。

guidance than to an impersonal

人们认为有血有肉的人类专家有一种健全的责任感，在落入黑暗的角落时，他们往往能够用创造性的火花照亮出路。

program.

我们的命运有一天或许会取决于确定性算法，对很多人来说这是一幅骇人的反乌托邦图景。

Professionals of flesh and blood are supposed to have a healthy sense of responsibility and, when caught in a dark corner, they can often illuminate the way out with a creative spark.

我们最好克服这种偏见。在一个充满数字化信息的世界里，算法比人更擅长分析复杂的交互作用。尽管算法缺乏创造性，但算法的连贯性和速度弥补这一点绰绰有余。

That our fate might one day lie

以航空为例，航空上的失误往往是致命的。20世纪60年代，工程师想出了一种方法来解决一种尤为常见和灾难性的飞行员操作失误。坐在机械性能良好的飞机的驾驶舱里，尽管飞行速度正常，飞行员也能完全控制飞机，飞机还是会撞上山坡或者撞进大海，显然飞行员察觉不到哪里出现了问题。

with deterministic algorithms is for many people a frighteningly dystopian vision.

这样的事故往往导致所有乘客失去生命，但后来几乎被完全杜绝。现在，安装在商用飞机上的近地警报系统可以探查飞行员无法察觉的障碍。依靠完善的地形图和传感器，该系统在黑暗中和恶劣天气下也能工作。同样重要的是，该系统能够跟踪飞机的位置，预测前行路径，使飞机不会遭遇难以探查的危险，比如在飞机起飞后骤然升高、让飞行员来不及应对的地形。而且，系统不像飞行员，不会受引擎故障或者航空管制指令等其他突发因素影响而分心。

It is a prejudice we would do well to overcome. In a world awash with digital information, algorithms are better than people at analysing complex interactions. What they lack in creativity, they more than make up for in consistency and speed.

在医疗诊断、气象学、金融学等广泛领域，已有几十项研究发现算法至少可以与人的主观分析匹敌，甚至胜过后者。研究者已经设计出能够估算出一些事件几率的算法，比如某个罪犯在释放后再次犯罪的几率，或者某家初创企业破产的几率。当研究者比较这些程序和人类观察家的预测能力时，发现人类不如这些程序。

Consider aviation, where mistakes are often deadly. In the 1960s engineers figured out a way to address a particularly common and catastrophic type of pilot error. Sitting in the cockpits of mechanically sound aircraft, while flying at the right speed and maintaining full control, they would crash into a mountainside or the sea, apparently oblivious to whatever had gone wrong.

而且这种结果恐怕还是以人类处于良好的状态为前提的。除了系统性失误，人类还会生病、疲劳、分心和厌倦。我们会变得情绪化。在极佳情况下，我们能保留并回忆起有限的信息。尽管我们包容多数这样的突发状况，但在越来越多的领域里，我们无需再忍受这些状况带来的局限性。容忍这些状况也不会让我们得到多少好处。然而我们似乎决心坚持下去，我们倾向于原谅“人为失误”，同时又要求算法绝对正确。

Such accidents, which often resulted in the loss of everyone on board, have since been all but eliminated. The ground proximity warning systems now fitted to commercial aircraft can see obstacles that pilots cannot, thanks to comprehensive terrain maps and sensors that work even in darkness and bad weather. As important, they keep track of the aircraft’s position and predict its path, so they are not caught out by hard-to-detect dangers such as terrain that is rising faster than an aircraft immediately after take-off. And, unlike pilots, they are never distracted by other urgent matters, such as a failing engine or an instruction from air traffic control.

看看人们对于无人驾驶汽车安全性的焦虑，即使美国国家公路交通安全管理局(National Highway Traffic Safety Administration)发现，人为失误，而非机械故障，才是美国每年发生的几乎所有交通事故的“关键性因素”。人们似乎依然更为信任其他人类（尽管他们知道人的逻辑和行为是有缺陷的），而不信任以一种不带任何偏见的方式运作的硬件和软件。

In fields as wide ranging as medical diagnosis, meteorology and finance, dozens of studies have found that algorithms at least match — and usually surpass — subjective human analysis. Researchers have devised algorithms that estimate the likelihood of events such as a particular convict lapsing back into crime after being released from custody, or a particular business start-up going bust. When they pitted the predictive powers of these programs against human observers, they found that the humans did worse.

当然，偏见是人类难以避免的逻辑错误的又一个例子。这种现象甚至还有一个名称——算法厌恶症，这个词是3名宾夕法尼亚大学(University of Pennsylvania)的研究者在2014年发明的。他们发现，即使看到在使用同样的数据时算法做出的预测比人更为准确，试验对象仍然更容易对程序失去信任，而不是对人类预测者失去信任。而按照逻辑判断，结果应该与此相反。显然，我们更情愿接受我们自身可预见的错误，而不是信任一种显然更有优势的方法。

And that, presumably, was on a good day. Aside from their systematic failings, people get sick, tired, distracted and bored. We get emotional. We can retain and recall a limited amount of information under the very best of circumstances. Most of these quirks we cherish, but in a growing number of domains we no longer need to tolerate the limitations they entail. Nor do we have much to gain from doing so. Yet we seem determined to persevere, tending to forgive “human error” while demanding infallibility from algorithms.

在范式以一种让人不适的方式发生偏转的时候，人类长期以来都惯于拒绝接受新的证据。如下两点对此大概没什么帮助：反乌托邦科幻小说伴随我们长大，同时公共知识分子不断坚称，人工智能——就像一个不新鲜的笑话所说的，人工智能是几乎所有计算机还无法做到的事情的简称——可能引起人类灭亡。

Witness the hand-wringing over the safety of driverless cars, even though the National Highway Traffic Safety Administration finds that human error — not mechanical failure — represents the “critical factor” in nearly all of the traffic accidents that occur in the US each year. People, it seems, would rather place their trust in other humans — whose logic and behaviour they know to be flawed — than in hardware and software that operates in a bias-free way.

我们越快学会相信算法，让算法来承担它们显然擅长的任务，对我们人类的福祉越有利。如果对未知的恐惧的确使怀疑论者对算法抱有一种不理性的偏见，那么了解算法的优势（和局限性）的专业人士应该承担起为它们辩护的任务。

This bias is, of course, just another instance of the logical error that humans struggle to avoid. There is even a name for it: “algorithm aversion”, a term three University of Pennsylvania researchers coined in a 2014 study. Even after witnessing algorithms make more accurate predictions than humans when using identical data, they found, test subjects were quicker to lose trust in the programs than in the human forecasters. Logically, the reverse should have been true. Apparently, we are more willing to live with our own predictable mistakes than to place our trust in a demonstrably superior method.

本文作者是算法投资管理公司Two Sigma的联合主席

Humans have a long record of rejecting new evidence as paradigms shift in uncomfortable ways. It probably does not help that we were raised on dystopian science fiction, or that public intellectuals keep asserting that artificial intelligence — shorthand, an old joke goes, for almost anything a computer cannot yet do — could cause the demise of the human race.