"What can machines learn, and what does it mean for occupations and the economy?"

by Brynjolfsson, Erik; Mitchell, Tom; Rock, Daniel (2018)

Abstract

Advances in machine learning (ML) are poised to transform numerous occupations and industries. This raises the question of which tasks will be most affected by ML. We apply the rubric evaluating task potential for ML in Brynjolfsson and Mitchell (2017) to build measures of "Suitability for Machine Learning" (SML) and apply it to 18,156 tasks in O*NET. We find that (i) ML affects different occupations than earlier automation waves; (ii) most occupations include at least some SML tasks; (iii) few occupations are fully automatable using ML; and (iv) realizing the potential of ML usually requires redesign of job task content.

Key Passage

Automation technologies have historically been the key driver of increased industrial productivity. They have also disrupted employment and the wage structure systematically. However, our analysis suggests that ML will affect very different parts of the workforce than earlier waves of automation. Furthermore, tasks within jobs typically show considerable variability in SML, while few (if any) jobs can be fully automated using ML. Machine learning technology can transform many jobs in the economy, but full automation will be less significant than the reengineering of processes and the reorganization of tasks. (p.46)

Keywords

Artificial Intelligence, Machine Learning, The Future Of Work, Worker Replacement, Automation

Themes

Economics, Automation

Links to Reference

• https://www.aeaweb.org/doi/10.1257/pandp.20181019
• https://pubs.aeaweb.org/doi/pdf/10.1257/pandp.20181019

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