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让人类管好算法
The rise of the algorithm need not be bad news for humans

来源:FT中文网    2017-05-17 06:44



        The science and technology committee of the House of Commons published the responses to its inquiry on “algorithms in decision-making” on April 26. They vary in length, detail and approach, but share one important feature — the belief that human intervention may be unavoidable, indeed welcome, when it comes to trusting algorithmic decisions.        4月26日,英国下议院(House of Commons)科学技术委员会公布了其“决策中的算法”调查收到的回复。它们在长度、内容和角度上各有不同,但都有一个重要信念:就信赖算法作出的决定而言,人类的干预可能是不可避免的,的确,人类干预将是件好事。
        Automation has already transformed agriculture and industry. Today, brown and blue collars are in a minority. About 80 per cent of US jobs are in services. Most of us deal with data and software in our working lives, not with bioware or hardware. The trouble is that computers eat data and software for breakfast. The digital revolution is now threatening white-collar jobs.        自动化已彻底改变了农业和工业。如今,棕领和蓝领工人是少数。约80%的美国就业岗位在服务业。我们大多数人会在工作中与数据和软件打交道,而不是农作物或重型工具。问题是电脑在对付数据和软件方面得心应手。数字革命正在威胁白领就业。
        This is not because digital makes technology intelligent, but because it makes tasks stupid — in other words, no intelligence is required to perform them successfully. Once this happens, algorithms can step in and replace us.        这不是因为数字让技术变得智慧,而是因为它让任务变得愚蠢,换句话说,成功执行这些任务不需要智慧。一旦这种情况出现,算法就能介入并取代我们。
        The consequence may be widespread unemployment today, but could also mean new jobs tomorrow. Unemployment in the eurozone is still above 9 per cent, for example. Yet in Germany, the demand for engineers is higher than the supply. The same holds true in the UK. And according to the World Bank, by 2030 the world will need 80m healthcare workers, double that in 2013.        后果可能是今天的大范围失业,但可能也意味着明日的新就业岗位。例如,欧元区失业率仍高于9%。然而在德国,工程技术人员供不应求。英国也是如此。根据世界银行(World Bank)的数据,到2030年,全球将需要8000万医疗工作者,是2013年的两倍。
        In a society in which algorithms and other automated processes are increasingly apparent, the important question, addressed by the select committee, is the extent to which we can trust such brainless technologies, which are regularly taking decisions instead of us.        在一个算法和其他自动化过程日益明显的社会里,下议院特别委员会聚焦的重要问题是,我们能够在多大程度上信任这些无脑技术?如今做决定的经常是它们,而不是我们。
        Now that white-collar jobs are being replaced, we may all be at the mercy of algorithmic errors — an unfair attribution of responsibility, say, or some other Kafkaesque computer-generated disaster.        既然白领就业正在被取代,我们都可能沦为算法错误的受害者,比如不公平的责任认定,或者其他一些“卡夫卡”式的由电脑造成的灾难。
        The best protection against such misfires is to put human intelligence back into the equation.        防范这类失败的最佳方法是让人类智慧重新做主。
        Trust depends on delivery, transparency and accountability. You trust your doctor, for instance, if they do what they are supposed to do, if you can see what they are doing and if they take responsibility in the event of things go wrong. The same holds true for algorithms. We trust them when it is clear what they are designed to deliver, when it is transparent whether or not they are delivering it, and, finally, when someone is accountable — or at least morally responsible, if not legally liable — if things go wrong.        信任取决于任务的完成、透明度和问责制度。例如,如果你的医生在尽心尽力,如果你看得到他们在做什么,如果他们在出现问题时承担责任,那么你就会信任你的医生。算法也是如此。如果它们完成的是应该完成的任务,如果它们在是否完成任务方面是透明的,如果在出现问题时,我们可以追究某人的责任,至少是道德上的责任(如果不是法律责任的话),我们就会信任它们。
        This is where humans come in. First, to design the right sorts of algorithms and so to minimise risk. Second, since even the best algorithm can sometimes go wrong, or be fed the wrong data or in some other way misused, we need to ensure that not all decisions are left to brainless machines. Third, while some crucial decisions may indeed be too complex for any human to cope with, we should nevertheless oversee and manage such decision-making processes. And fourth,        这就是人类要介入的地方。首先,要设计正确的算法,以此降低风险。其次,即便是最好的算法有时也可能出错,或者被导入错误的数据或者以其他方式被不当利用,因此我们需要确保不把所有决策都交给无脑的机器。第三,尽管一些关键决定可能的确过于复杂,人类无力应对,但我们应监督并管理这些决策过程。第四,尽管决定是由算法做出的,但在出现问题时,这不能成为无视只有人类能够提供的洞见和理解的理由。
        the fact that a decision is taken by an algorithm is not grounds for disregarding the insight and understanding that only humans can bring when things go awry.        简言之,我们需要一个由人类监督的设计、控制、透明度和问责体系。这不需要意味着拒绝数字技术提供的帮助。毕竟,尽管电脑下棋的技能可能超过人类,但人机合作才是无敌的。
        In short, we need a system of design, control, transparency and accountability overseen by humans. And this need not mean spurning the help provided by digital technologies. After all, while a computer may play chess better than a human, a human in tandem with a computer is unbeatable.        显然,下议院特别委员会调查收到的回复是好消息。它们表明,未来人类将有大量的智慧工作要做。但填补这些新岗位的不是白领员工。只有专家才能应对这种新的数字环境及其人工代理。算法将是新的羊群。我们未来的工作将是做行业的牧羊人。“绿领工人”时代正在到来。
        The responses to the select committee inquiry are clearly good news. They show that there is plenty of intelligent work for humans to do in the future. But it won’t be white-collar workers filling these new positions. It will be experts who can take care of the new digital environment and its artificial agents. Algorithms are the new herd. Our future jobs will be in the shepherding industry. The age of green collars is coming.        本文作者是牛津大学(University of Oxford)哲学和信息伦理学教授
        The writer is professor of philosophy and ethics of information at the University of Oxford        译者/梁艳裳
                
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