The Analysis of Automation by Means of Automation: A Machine Learning Approach to Job Tasks.

自20世纪80年代以来，计算机和自动化对工作生活产生了巨大的影响，最新的技术变革强烈表明，这种趋势将继续下去。自动驾驶汽车、基于“无人机”的机器人技术的全面改进以及医疗诊断“专家系统”的出现等发明创造了这样一种前景：自动化将很快对以前能够被机器取代的工作产生影响。反过来，收入和就业需求的性质可能会以意想不到的方式发生变化，从而可能导致我们的福利制度和总体社会凝聚力发生剧变。特别是，更多的低技能工作可能会被机器取代，而工资分配中高端的工作可能面临新的威胁。由于新技术减少了在服务方面的实际共同地点和面对面互动的需要，离岸外包的进程也可以加快。这个项目提供了一种新的自动化分析，它本身就是基于自动的、计算机驱动的分类技术。自1938年以来，美国就业专家一直在编制并定期更新丰富、详细的任务描述，涉及约1.2万个工作岗位。这些职位描述及其相关的数字“特征分数”已经成为劳动力市场新“工作任务”观点的基础。这种作业任务视图将作业分解为任务包，最好的总结是：第一是“常规”任务（可以被编码并编程到某种类型的机器中），第二是“非常规”任务（那些需要高级手工或分析技能的任务，还不能被编程到计算机中）。反过来，这种任务观在解释劳动力市场的工作模式方面取得了很大成功。本项目将提出的分析将直接基于这些专家撰写的职位数据库中的文本职位描述。与“大数据”方法相关的机器学习方法将用于分解职位描述文本，以发现构成我们职业的任务的潜在结构。例如，这些方法将使我们能够了解与计算机相关的短语和概念在不同职业中的传播。这些统计方法基于对聚集和重复文本的自动“模式识别”原理，因此具有讽刺意味的是，自动化方法本身将用于研究经济中的自动化结构。这种方法的主要贡献是提供了职业内部变化的衡量标准。最近对劳动力市场的分析表明，在“群体内”一级发生了很大的变化，即在特定教育、行业或职业类别内的分散。在这个项目中使用的方法将通过研究文本职位描述所建议的任务模式来详细测量职业中发生的变化。该项目还具有新颖的传播计划。将采用通常的学术出版和介绍工具，同时开展全面的媒体宣传活动，以提高公众对该研究的认识。首席研究员在学术出版和公众参与这两个领域都有良好的记录。数据/政策博客和在线代码存储库将支持新的公开可用数据，从而增强这一点。其目标是将“开源代码”代码共享文化引入这些实践尚未扎根的社会科学领域。

Computers and automation have had a massive impact on working life since the 1980s and the latest technological changes strongly suggest that this trend will continue. Inventions such as the self-driving car, general improvements in 'drone'-based robotics, and the emergence of 'expert systems' for medical diagnosis create the prospect that automation will soon be having an impact on jobs that were previously resilient to being replaced by machines. In turn, the nature of earnings and job demand could change in unexpected ways that could cause upheaval for our welfare system and general social cohesion. In particular, even more low skill jobs could be replaced by machines and jobs in the middle and upper end of the wage distribution could face new threats. The process of offshoring could also be accelerated as new technology reduces the need for physical co-location and face-to-face interactions in services.This project provides a new analysis of automation that is itself based on automatic, computer driven classification techniques. Since 1938, US job experts have been compiling and regularly updating rich, detailed task descriptions across approximately 12,000 jobs. These job descriptions and their associated numerical 'characteristics scores' have been the basis of a new 'job tasks' view of the labour market. This job tasks view has split jobs into bundles of tasks best summarised by firstly 'routine' tasks (ones that can be codified and programmed into a machine of some type) and secondly 'nonroutine' tasks (those tasks that require advanced manual or analytical skills that cannot yet be programmed into a computer). In turn, this task view has had much success in explaining the pattern of jobs in the labour market. The analysis that this project will put forward will be based directly on the text job descriptions in these expert-written job databases. Machine learning methods associated with the 'Big Data' approach will be used to decompose the job description text to discover the underlying, latent structure of the tasks that make up our occupations. For example, these methods will allow us to pick up the diffusion or spread of phrases and concepts associated with computers across occupations. These statistical methods are based on the principle of automated 'pattern recognition' of clustered and repeated text, so in an ironic twist automated methods will themselves be used to study the structure of automation in the economy. The main contribution of this approach is to provide measures of what has been changing within occupations. Recent analysis of the labour market has shown that much change has been occurring at the 'within group' level, that is, dispersion inside specific educational, industry or occupational categories. The methods used in this project will measure in detail what has been changing within occupations by studying the pattern of tasks as suggested by the text job descriptions.The project also features a novel dissemination plan. The usual tools of academic publication and presentation will be deployed along with a comprehensive media campaign to raise public awareness of the research. The Principal Investigator has a strong track record in both these areas of academic publication and public engagement. This will be enhanced by new publicly available data that will be supported with a data/policy blog and online code repository. The aim will be to bring the 'open source' code-sharing culture into the social sciences where these practices have not yet taken root.