What Can Machines Learn, and What Does It Mean for Occupations and the Economy?

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: <br>1) ML affects different occupations than earlier automation waves, <br>2) most occupations include at least some SML tasks, <br>3) few occupations are fully automatable using ML, and <br>4) realizing the potential of ML usually requires redesign of job task content.

robots to reduced employment and wages in local labor markets. 1 While advances in ML are impressive, and automation is already having significant effects on many parts of the workforce, we are far from artificial general intelligence (AGI) which would match humans in all cognitive areas. This raises the question of which tasks will be most affected by ML and which will be relatively unaffected.
In particular, a key insight of Autor, Levy, and Murnane (2003) is that an occupation can be viewed as a bundle of tasks, some of which offer better applications for technology than others. As with studies of routine task automation, the impact of machine learning on employment is a function of the suitability of machine learning for specific work activities.
Furthermore, as noted by Levy (2017), the differential effectiveness of ML in different tasks suggests that the impact of ML diffusion will be uneven across occupations.
We first examine the channels by which ML can affect the workforce. Next, we apply Brynjolfsson and Mitchell's (2017)  unleashing ML potential will require significant redesign of the task content of jobs, as SML and non-SML tasks within occupations are unbundled and rebundled.
Our findings suggest that a shift is needed in the debate about the effects of AI on work: away from the common focus on full automation of jobs and pervasive occupational replacement toward the redesign of jobs and reengineering of business processes. Our evidence suggests that ML technologies will indeed be pervasive, but that within jobs, the SML of work tasks varies greatly. We suggest that variability in task-level SML is an indicator for the reorganization of a job, as the high and low SML tasks within a job can be separated and re-bundled. Our focus should be not (just) on automation, but on job redesign.

I. Machine Learning and Task Automation
Most of the recent progress in ML performance has been made by a specific class Electronic copy available at: https://ssrn.com/abstract=3224100 Electronic copy available at: https://ssrn.com/abstract=3224100 of algorithms called deep neural networks, or more generally, deep learning systems. 2 Although the basic structure of some of these models is decades old, significant new algorithmic advances have also been made. Performance measurement is directly related to the industrial potential for reinforcement learning algorithms as well. For instance, researchers at Google DeepMind report that they have implemented a neural net system that reduced cooling costs by 40% compared to the same data center when it was optimized by their human engineers (see: https://deepmind.com/blog/deepmind-ai-reduces-google-datacentre-cooling-bill-40/) 5 For instance, workers may need to be compensated for taking on bundles of tasks with noisier average performance when machines handle measurable tasks. This has the implication that over time worker performance will become harder to evaluate since, the most measurable tasks tend to be the most suitable for ML. Brynjolfsson, Mitchell, and Rock (2018)  There are a number of important conceptual caveats to this application of the SML rubric.
The rubric is mostly silent on societal factors influencing ML adoption. Additionally, we are focused on the near-term. 8 Matching the state of the art in ML in the future will require updating the rubric accordingly. 6 Rubric details are available in the supplementary materials. 7 The supplementary materials detail how the raw CrowdFlower dataset is built and processed. This dataset is sourced from our companion paper (Brynjolfsson, Mitchell, and Rock 2018). In addition to our measures included here (based on averages of median ratings of activities), we also evaluate more complex boolean combinations of the scores in the companion paper.
8 For example, we have considered extensive physical activity a challenge for implementation of machine learning. Table 1 summarizes the SML measures for occupations, tasks, and activities. Table 2 presents the occupations with the 5 highest and 5 lowest values for SML. Economists, for the concerned, are close to average (SML of 3.46). The variance of occupation-level SML is considerably lower than that of the tasks.
Job bundling of tasks provides some diversification with respect to machine learning exposure. Figure 1   Acemoglu, Daron, and Pascual Restrepo. 9 We might see, for example, large-scale machine learning platform companies contracted to automate aspects of various jobs. The wage and employment effects of these contracts are ambiguous given possible channels of demand elasticity, complementary task efforts, and substitutes.