Paid Crowdsourcing as a Vehicle for Global Development
Paid Crowdsourcing as a Vehicle for Global Development
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付费众包作为全球发展的工具
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发表时间：
2011
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通讯作者：
James Davis
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作者：
Bill Thies;Aishwarya Ratan;James Davis
通过将远程工作者与全球市场联系起来，有偿众包有可能改善世界各地贫困社区的收入和生计。然而，要实现这一潜力还有很长的路要走。到目前为止，微任务平台上的大多数员工都来自相对富裕的背景，对低收入人群的影响有限。在这份立场文件中，我们概述了一个研究议程，以扩大发展中国家低收入工人的非正式，有偿微任务的好处。这一目标将需要沿着多条战线进行研究，包括众包平台本身、它们对用户生计的影响以及它们对大量人群的可扩展性。虽然有许多挑战需要克服，但回报是巨大的。我们认为，重新关注低收入工人对于释放付费众包平台的潜在规模和影响至关重要。1.有理由相信，有偿众包1可能特别有利于发展中国家的低收入工人。与大多数就业机会不同，亚马逊土耳其机器人（MTurk）等在线市场不需要雇主和雇员之间的地理位置。开始工作不需要正式合同;就业的唯一标准是有能力完成手头的任务。此外，工作时间完全灵活，工人只要有多余的时间就可以赚钱。能够访问移动的计算机的工人甚至可以在他们的日常通勤期间或者当他们在其他工作期间空闲时（例如，司机可以在等待客户时工作，或者店主可以在商店空着时工作）。出于所有这些原因，人们可以期望在线微任务服务降低低收入环境中就业的准入门槛，从而可能提高其他弱势群体的社会经济地位。在本文中，我们认为有偿众包是提供小任务，以换取货币支付通过网站，如亚马逊土耳其机械，CloudCrowd，ShortTask等，虽然我们不知道所使用的平台，我们限制我们的注意力在非正式的设置，工人选择一次工作一个任务，而不是与公司签订长期合同。但是，付费众包是否发挥了其潜力，使经济金字塔底层的人受益？今天的答案是“是”和“不是”。从积极的一面来看，有文件表明MTurk上超过三分之一的工人来自印度[4，5]，这表明该服务可以在发展中国家取得进展。在我们自己对200名印度土耳其人的调查中，我们发现了MTurk从根本上改变了我们受访者生计的几个案例。例如，一位来自印度加尔各答的26岁大学毕业生描述了他是如何通过Mechanical Turk每年赚1860美元的：我来自一个中产阶级家庭。毕业后，我到处找工作，但都失败了。但当我发现MTurk时，它改变了我的生活。它帮了我很多。然而，尽管有这样的轶事，付费众包在发展中国家的影响仍然有限。虽然许多Turker工作在印度，但在我们的调查中，我们发现这些工人的社会经济地位相对较高：80%拥有学士学位或更高学位，92%家中有PC和互联网连接，他们的年收入中位数为2700美元。这比一般印度人的情况要好得多。印度只有6%的劳动力拥有学士学位，只有6%的家庭拥有电脑和互联网接入，人均年收入为1100美元。为了对贫穷社区产生影响，土耳其机器人等系统需要向那些受过较低正规教育（中学和高中教育）、只掌握计算机和英语基本技能的人开放。今天，付费众包的好处在很大程度上是这一人群无法企及的。在本文中，我们概述了一个议程，以塑造付费众包的演变成为社会经济发展的工具。我们的议程受到了我们最近研究的很大影响，该研究考察了Mechanical Turk对印度低收入工人的可用性[5]。该研究发现，虽然MTurk上的一些任务并没有超出低收入工人的认知能力，但语言和用户界面是这一人群在MTurk上赚钱的重大障碍。通过简化用户界面和任务说明，并将所有内容翻译成当地语言，我们证明了低收入工人可以实现更高的任务完成率。尽管如此，由于访问计算机的成本和其他障碍，这些工人仍然难以在MTurk上可靠地赚钱。在扩大我们的改革方面，还有许多问题有待解决。
By connecting remote workers to a global marketplace, paid crowdsourcing has the potential to improve earnings and livelihoods in poor communities around the world. However, there is a long way to go before realizing this potential. To date, most workers on microtasking platforms come from relatively well-off backgrounds, and there has been limited impact on low-income individuals. In this position paper, we outline a research agenda to extend the benefits of informal, paid microtasking to low-income workers in developing countries. This goal will require research along multiple fronts, spanning the crowdsourcing platforms themselves, their impact upon users’ livelihoods, and their scalability to large populations. While there are many challenges to overcome, the rewards are great. We believe that a new focus on low-income workers is critically important to unlock the potential scale and impact of paid crowdsourcing platforms. INTRODUCTION There are reasons to believe that paid crowdsourcing1 could be of particular benefit to low-income workers in developing countries. Unlike most employment opportunities, online marketplaces such as Amazon Mechanical Turk (MTurk) do not require geographic co-location between employer and employee. There is no formal contract needed to commence work; the only criterion for employment is the ability to complete the task at hand. In addition, the working hours are completely flexible, allowing workers to earn money whenever they have extra time. Workers with access to a mobile computer may even be able to earn money during their daily commute, or when they are idle during other jobs (e.g., a driver could work while waiting for a client, or a shopkeeper could work while the store is empty). For all of these reasons, one would expect online microtasking services to lower the barrier to entry for employment in low-income settings, potentially boosting the socio-economic standing of otherwise disadvantaged populations. In this paper, we consider paid crowdsourcing to be the provision of small tasks in exchange for monetary payment via websites such as Amazon Mechanical Turk, CloudCrowd, ShortTask, etc. While we are agnostic as to the platform used, we restrict our attention to the informal setting in which workers choose to work on one task at a time, rather than having a long-term contract with a company. But has paid crowdsourcing delivered on its potential to benefit those at the bottom of the economic pyramid? The answer today is both “yes” and “no”. On the positive side, it has been documented that over a third of workers on MTurk are based in India [4, 5], suggesting that the service can make inroads in a developing-country context. And in our own survey of 200 Indian Turkers [5], we uncovered several cases in which MTurk had fundamentally changed the livelihood of our respondents. For example, a 26-year old college graduate from Kolkata, India, describes how he came to earn $1860 per year on Mechanical Turk: I’m from a middle class family. After completing my degree I looked for job everywhere but failed. But when I found MTurk, it changed my life. It helped me a lot. However, despite such anecdotes, the impact of paid crowdsourcing in developing countries remains limited. While many Turkers are based in India, in our survey we found that these workers have a relatively high socio-economic standing: 80% have a Bachelor’s degree or higher, 92% have a PC and Internet connection in their home, and their median annual income is $2700. This is substantially better than the position of the average Indian. Only 6% of India’s workforce has a Bachelor’s degree, only 6% of households have a computer and Internet access, and average per capita income is $1100 per year [5]. To have an impact on poor communities, systems such as Mechanical Turk will need to be accessible to those with lower formal education (secondary and higher secondary schooling) and only basic skills with computers and the English language. Today, the benefits of paid crowdsourcing are largely out of reach for this population. In this paper, we outline an agenda to shape the evolution of paid crowdsourcing into a vehicle for socio-economic development. Our agenda is heavily influenced by our recent study, which examined the usability of Mechanical Turk for low-income workers in India [5]. The study found that while there are tasks on MTurk that are not beyond the cognitive capabilities of low-income workers, the language and user interface represent significant barriers for this population to earn money on MTurk. By simplifying the user interface and task instructions, and translating all content into the local language, we demonstrated that low-income workers can achieve much higher rates of task completion. Still, it remains challenging for such workers to reliably earn money on MTurk, due to costs of accessing computers and other barriers. Many questions also remain in scaling up our re-