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

Brynjolfsson, Erik; Mitchell, Tom M.; Rock, Daniel

doi:10.1257/pandp.20181019

Cited by 297 publications

(339 citation statements)

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“…Deep learning has more recently made great progress in such applications as speech and language understanding, computer vision, and event and behavior prediction (Goodfellow et al, 2016). These rapid technological advances and the promise of automation and human-intelligence augmentation (Jordan, 2019) reignited debates on AI's impact on jobs and markets (Brynjolfsson et al, 2018;Samothrakis, 2018;Schlogl and Sumner, 2018) and the need for AI governance (Aletras et al, 2016;Benjamins et al, 2005).…”

Section: Ai: Machine Learning and Nlpmentioning

confidence: 99%

Deep Learning for Political Science

Chatsiou¹,

Mikhaylov

2020

The SAGE Handbook of Research Methods in Political Science and International Relations

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IntroductionPolitical science, and social science in general, have traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. More recently, increasingly available quantities of data are being combined with improved algorithms and affordable computational resources to predict, learn, and discover new insights from data that is large in volume and variety. New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories and evaluating the impact of interventions and programs in a more dynamic and effective way.Applications using large volumes of structured and unstructured data are becoming common in government and industry, and increasingly also in social science research.

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Section: Ai: Machine Learning and Nlpmentioning

confidence: 99%

Deep Learning for Political Science

Chatsiou¹,

Mikhaylov

2020

The SAGE Handbook of Research Methods in Political Science and International Relations

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“…To test for robustness of our results, we use an alternative measure of automatability at the occupational level developed by Brynjolfsson, Mitchell, and Rock (2018). The "suitability for machine learning" (SML) index measures the degree to which machine learning has a potential to eliminate tasks within an occupation.…”

Section: A Estimating the Probability Of Automationmentioning

confidence: 99%

“…15 SML index ranges from 3.19 to 3.63 in our sample, which gives us a threshold of 3.49 for the high-risk group. (2017); Brynjolfsson, Mitchell, and Rock (2018); PIAAC survey; and IMF staff estimates. Note: The probability of automation and SML are estimated using an expectation-maximization (EM) algorithm that relates individual characteristics (age, education, training, among other) and job task characteristics to occupational level risk of automation.…”

Section: Cross-country Differencesmentioning

confidence: 99%

“…But computerization of white-collar services in advanced economies has accelerated in recent years (Acemoglu and Restrepo, 2018). At the same time, progress in machine learning is further expanding the set of activities that can be performed more efficiently by computers than humans (Brynjolfsson, Mitchell, and Rock, 2018), suggesting a significantly broader scope for task automation over the medium-term. What are the gender implications of this changing nature of work?…”

Section: Introductionmentioning

confidence: 99%

“…Austria, Singapore, and Ireland do not have information on payment schemes; Cyprus, Spain, France, and Italy do not collect data on problem solving skills, and Canada does not collect information on payment scheme or job's educational requirement.14 As opposed to Frey and Osborne's (2017) method of categorizing each occupation based on the binary choice-automatable or non-automatable-Brynjolfsson, Mitchell, and Rock (2018) distribute a 23-question ©International Monetary Fund. Not for Redistribution…”

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confidence: 99%

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Is Technology Widening the Gender Gap? Automation and the Future of Female Employment

Brussevich¹,

Dabla‐Norris²,

Khalid³

2019

IMF Working Papers

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Using individual level data on task composition at work for 30 advanced and emerging economies, we find that women, on average, perform more routine tasks than men-tasks that are more prone to automation. To quantify the impact on jobs, we relate data on task composition at work to occupation level estimates of probability of automation, controlling for a rich set of individual characteristics (e.g., education, age, literacy and numeracy skills).Our results indicate that female workers are at a significantly higher risk for displacement by automation than male workers, with 11 percent of the female workforce at high risk of being automated given the current state of technology, albeit with significant cross-country heterogeneity. The probability of automation is lower for younger cohorts of women, and for those in managerial positions. JEL Classification Numbers: E24, J16, J23, J24, O33

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Artificial intelligence and the work–health interface: A research agenda for a technologically transforming world of work

Jetha

Bakhtari

Rosella

et al. 2023

American J Industrial Med

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The labor market is undergoing a rapid artificial intelligence (AI) revolution. There is currently limited empirical scholarship that focuses on how AI adoption affects employment opportunities and work environments in ways that shape worker health, safety, well‐being and equity. In this article, we present an agenda to guide research examining the implications of AI on the intersection between work and health. To build the agenda, a full day meeting was organized and attended by 50 participants including researchers from diverse disciplines and applied stakeholders. Facilitated meeting discussions aimed to set research priorities related to workplace AI applications and its impact on the health of workers, including critical research questions, methodological approaches, data needs, and resource requirements. Discussions also aimed to identify groups of workers and working contexts that may benefit from AI adoption as well as those that may be disadvantaged by AI. Discussions were synthesized into four research agenda areas: (1) examining the impact of stronger AI on human workers; (2) advancing responsible and healthy AI; (3) informing AI policy for worker health, safety, well‐being, and equitable employment; and (4) understanding and addressing worker and employer knowledge needs regarding AI applications. The agenda provides a roadmap for researchers to build a critical evidence base on the impact of AI on workers and workplaces, and will ensure that worker health, safety, well‐being, and equity are at the forefront of workplace AI system design and adoption.

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

Cited by 297 publications

References 10 publications

Deep Learning for Political Science

Deep Learning for Political Science

Is Technology Widening the Gender Gap? Automation and the Future of Female Employment

Artificial intelligence and the work–health interface: A research agenda for a technologically transforming world of work

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