Brynjolfsson & Mitchell (2017)

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Science(peer-reviewed)·science.sciencemag.org/content/358/6370/1530

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A Science journal paper offering a practical framework for estimating which tasks are automatable by ML; frequently cited in AI labor market and economic impact discussions relevant to AI governance and deployment policy.

Metadata

Importance: 62/100journal articleprimary source

Summary

Brynjolfsson and Mitchell develop a rubric for assessing which tasks within occupations are amenable to machine learning automation, identifying key characteristics that make tasks suitable or unsuitable for ML. They apply this framework to analyze the potential economic and labor market impacts of advancing AI capabilities. The paper provides a structured approach to understanding AI's differential effects across job types and economic sectors.

Key Points

•Introduces the 'ML suitability' rubric: tasks amenable to ML involve well-defined inputs/outputs, large training datasets, and do not require complex reasoning chains.
•Finds that many occupations contain a mix of ML-suitable and ML-unsuitable tasks, making wholesale job displacement less likely than task-level restructuring.
•Highlights that ML automation may affect high-skill cognitive jobs more than previous waves of automation, with different distributional effects than prior technology shocks.
•Argues that understanding task-level characteristics is more predictive of automation risk than broad occupational categories.
•Raises policy implications around labor market transitions, education, and the need for new frameworks to assess AI's economic impact.

Cited by 1 page

Page	Type	Quality
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Contents

## Abstract

Digital computers have transformed work in almost every sector of the economy over the past several decades ( _1_). We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a “general purpose technology,” like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities ( _2_), there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do \[see the supplementary materials (SM)\]. Although parts of many jobs may be “suitable for ML” (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent “end of work” as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.

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## Supplementary Material

### Summary

Supplementary Text

Table S1

### Resources

File(aap8062-brynjolfsson-sm.pdf)

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## References and Notes

1

National Academies of Sciences, Engineering, and Medicine, _Information Technology and the U.S. Workforce: Where Are We and Where Do We Go from Here?_ (National Academies Press, Washington, DC, 2017).

[Google Scholar](https://scholar.google.com/scholar_lookup?title=Information+Technology+and+the+U.S.+Workforce%3A+Where+Are+We+and+Where+Do+We+Go+from+Here%3F&publication_year=2017)

2

E. Brynjolfsson, D. Rock, C. Syverson, Artificial Intelligence and the Modern Productivity Paradox: A Class of Expectations and Statistics, _NBER Working Paper 24001_ (National Bureau of Economic Research, Cambridge, MA, 2017).

[Google Scholar](https://scholar.google.com/scholar_lookup?title=Artificial+Intelligence+and+the+Modern+Productivity+Paradox%3A+A+Class+of+Expectations+and+Statistics&author=E.+Brynjolfsson&author=D.+Rock&author=C.+Syverson&publication_year=2017)

3

S. Legg, M. Hutter, _Frontiers in Artificial Intelligence and Applications_ **157**, 17 (2007).

[Google Scholar](https://scholar.google.com/scholar_lookup?author=S.+Legg&author=

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