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科学家宣称找到了解决难民危机的办法

更新时间:2018/2/4 11:53:22 来源:本站原创 作者:佚名

The algorithm that could help tackle the refugee crisis
科学家宣称找到了解决难民危机的办法

There are a total of 65.6 million forcibly displaced people in the world today, fleeing conflict, persecution and corruption, according to the United Nations Refugee Agency – an all-time record.

根据联合国难民署(United Nations Refugee Agency)的数据,全世界目前因为冲突、迫害和腐败等各种原因而背井离乡的难民总数约为6,560万人,创下历史最高纪录。

In the countries where many displaced people dream of starting new, successful lives such as Australia, the US, the UK and Germany, refugee resettlement is a complicated, contentious issue.

澳大利亚、美国、英国和德国都是难民心目中的热门国家,他们渴望在那里开始美好的新生活。但如何安置难民却成为这些国家面临的最具争议的复杂问题之一。

Political pushback aside, there are huge obstacles that jam the system. Many nations currently place incoming refugees haphazardly, dispatching them to disparate regions based purely on whether these areas have enough space to accommodate extra people. But there’s no guarantee that these areas will provide enough jobs for the new arrivals – and being unemployed can be a huge barrier for a refugee to find jobs and afford to live in their new homeland.

抛开政治因素不谈,这套系统面临很大的障碍。许多国家目前安置难民的方式都很随机,只会考虑某个地方是否有足够的地方容纳额外的人口,据此来把难民分配到不同的地区。但这样却不能确保这些地方能为这些难民提供足够的工作——失业会成为难民在新家正常生活的巨大障碍。

But a team of researchers at Stanford University, ETH Zurich and Dartmouth College has come up with a system they believe can vastly improve the job prospects of newly settled refugees.

但斯坦福大学(Stanford University)的一个研究团队开发了一套系统,他们认为可以帮助新安置的难民极大地改善就业前景。

Outlined in a paper published in the journal Science today, the team have created a data-driven algorithm that learns how to allocate displaced people to where they are much more likely to find jobs. It hasn’t been tested in the real world yet, but the researchers believe it could boost the likelihood of employment for each family by up to 70%.

该团队发表在《科学》(Science)杂志的论文中表示,他们开发了一套数据驱动的算法,可以学习如何优化难民分配方式,以便大幅提高他们找到工作的概率。该系统尚未在现实世界中测试,但研究人员相信,这最多可以把每个家庭的就业率提升到70%。

Currently, a bureaucrat will use a spreadsheet to assign families to locations based on capacity constraints, says Jens Hainmueller, one of the researchers at Stanford’s Immigration Policy Lab. “There’s a bed in Minnesota, you go to Minnesota. There’s no purposeful matching.”

斯坦福移民政策实验室研究员延斯·海恩穆勒(Jens Hainmueller)表示,政府目前根据容量限制,用电子表格来决定难民家庭前往何处。"明尼苏达(Minnesota)有一张床,你就去明尼苏达。但并没有形成有目的的匹配。"

If a resettlement agency could analyse an immigrant’s demographic profile and send them to a town, city or region where they’d be more likely to find a job, they would be a better chance to succeed.The team analysed figures from two developed countries: The US (using data from more than 30,000 refugees aged 18-64, who arrived between 2011 to 2016) and Switzerland (more than 20,000 refugees from 1999 to 2013). The algorithm was built on the likelihood that individual refugees found employment in their host country.

如果负责安置的机构能够分析难民的人口统计学资料,然后把他们送到更容易找到工作的乡镇、城市或地区,那就可以提高成功率。该团队分析了两个发达国家的数据,分别是美国(使用三万多18至64岁的难民数据,他们在2011至2016年间来到美国)和瑞士(涵盖1999至2013年来到该国的两万多难民)。该算法可以计算每个难民在新的国家找到工作的可能性。

First, the team looked at the refugees’ demographic data: education, age, gender and English fluency. From there, they looked for “synergies” between these characteristics and regions with high employment rates for people with those specific characteristics.

首先,该团队会查看难民的人口统计学数据:教育程度、年龄、性别、英语流利度。之后,他们便会寻找"协同效应",将这些特征与具备这些特征的人更容易找到工作的地区进行匹配。

Then they found trends: if, for example, certain African refugees spoke French, they’d obviously find work more easily in French-speaking Swiss cantons (regions) than in German-speaking ones.

之后,他们便会发现各种趋势:例如,某些非洲移民会说法语,他们在瑞士法语区比在德语区更容易找到工作。

And voila – using the algorithm, a resettlement agency could analyse an immigrant’s demographic profile and use available data to place them where they’d be a great deal more likely to succeed.

通过算法,安置机构可以分析难民的人口统计学资料,并利用可用的数据将这些人分配到他们最有可能成功的地方。

“If there is a meatpacking plant that employs young male refugees, and there’s a demand for that, that algorithm would pick that up,” says Hainmueller.

"如果有一家肉类工厂聘用了年轻男性难民,而且有这方面的需求,这套算法就能挑选出来。"海恩穆勒说。

A simple way of thinking about it, the researchers say, is to use the example of two young Afghani men with the same education level and age who are sent to two different locations in their new country. One finds work in place A, the other doesn’t in place B. The team’s machine-learning algorithm learns from that, and next time, if a third person arrives with a similar background, the programme will then know to send him to place A if possible.

研究人员表示,想要让这套系统通俗易通,可以用两个年轻阿富汗男性的例子来说明。这两个人的教育程度和年龄相同,但到了新的国家之后,被送到了两个不同的地方。被送到A地的人找到了工作,送到B地的人没找到。该团队的机器学习算法了解到这个情况,于是,下次有第三个拥有相似背景的人出现时,如果有可能,程序就会自动把他送到A地。

To be sure, every situation and each individual is different. The team acknowledges that a human official might sometimes have to override a placement match. In that way, like a lot of AI, it complements humans rather than replace them.

需要明确的是,每个环境和每个人都各不相同。该团队也承认,人类官员有时候不得不推翻系统的匹配结果。与很多人工智能一样,该系统在这种情况下的作用就是为人类提供补充,而不是取代人类。

“The machine learning techniques we are using are extremely flexible,” says Kirk Bansak, another member of the team. “They are able to discover and find [patterns] in really complex data.”

"我们正在使用的机器学习技术非常灵活。"该团队另外一名成员科克·班萨克(Kirk Bansak)说,"他们可以在十分复杂的数据中寻找和发现各种模式。"

For example, had the algorithm been implemented in the US between 2011 and 2016, the researchers believe the average employment rate could have risen from 34 to 48% (a 41% increase). In Switzerland, it might have been boosted from 15% employed to 26%.

例如,如果这套算法在2011至2016年间部署在美国,研究人员认为平均就业率就会从34%增长到48%(增幅达41%)。瑞士可能从15%增长到26%。

“What we see is that refugees are much more likely to find work, they learn the language more quickly, they integrate more quickly, and are also not going to end up taking a lot of resources in terms of health benefits. They are economically integrated, pay taxes and make contributions to society,” Hainmueller says.

"我们发现难民更有可能找到工作,他们学习语言的速度也会加快,融合速度加快,而且最终不会占用太多健康福利资源。他们会从经济上融合起来,还会纳税,并为社会做出贡献。"海恩穆勒说。

Of course, more research is needed, but the team is working with governments and organisations to set up pilot programmes to test the algorithm’s effectiveness in the real world. Eventually, they hope for places like the US and Switzerland to use the algorithm (the code for which is available to organisations for free, the university says) when matching refugees to their new homes. The Swiss government has publicly expressed interest, the team says, and they're also in talks two resettlement agencies in the US to implement the system.

当然,仍然需要展开更多研究,但该团队正在与政府和各类组织合作建立试点项目,以便测试实际应用效果。最终,他们希望美国和瑞士等地可以使用这套算法(斯坦福大学表示,可以免费提供运算代码给相关组织使用)来匹配难民和他们的新家。该团队称,瑞士政府已经公开表达了兴趣。他们也在与美国的安置机构就部署这套系统展开沟通。

If implemented, the Stanford researchers hope their algorithm can strengthen the workforce and revitalise a local economy – it could potentially help nations handle a thorny political issue.

如果能够实际部署,斯坦福研究人员希望他们的算法能够巩固劳动力市场,为地方经济重新赋予活力——这有望帮助国家处理棘手的政治问题。

“We have the historical data anyway,” Hainmueller says. “We may as well learn from it.”

"总之,我们有历史数据。"海恩穆勒说,"或许可以从中学习一些东西。"

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