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

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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.