In their seminal paper, Frey and Osborne quantified the automation of jobs, by assigning each job in the O*NET database a probability to be automated. In this paper, we refine their results in the following way: Every O*NET job consists of a set of tasks, and these tasks can be related. We use a linear program to assign probabilities to tasks, such that related tasks have a similar probability and the tasks can explain the computerization probability of a job. Analyzing jobs on the level of tasks helps comprehending the results, as experts as well as laymen can more easily criticize and refine what parts of a job are susceptible to computerization.
The seminal paper by Frey and Osborne is the first to make quantitative claims about the future of jobs @cite_17 . Together with 70 machine learning experts, Frey and Osborne first manually labeled 70 out of 702 jobs from the database as either automatable'' or non automatable''. This labeling was, as the authors admit, a subjective assignment based on eye balling'' the job descriptions from . Labels were only assigned to jobs where the whole job was considered to be (non) automatable, and to jobs where the participants of the workshop were most confident. To calculate the probability for non-labeled jobs, Frey and Osborne used a probabilistic classification algorithm. They chose 9 properties from as features for their classifier, namely Finger Dexterity'', Manual Dexterity'', Cramped Work Space, Awkward Positions'', Originality'', Fine Arts'', Social Perceptiveness'', Negotiation'', Persuasion'', and Assisting and Caring for Others''.
@cite_17
2526781987
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupations probability of computerisation, wages and educational attainment.