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dc.contributor.advisorGeoffrey G. Parker.en_US
dc.contributor.authorKansu, Hazal Mine.en_US
dc.contributor.otherMassachusetts Institute of Technology. Institute for Data, Systems, and Society.en_US
dc.contributor.otherTechnology and Policy Program.en_US
dc.date.accessioned2020-04-13T18:33:36Z
dc.date.available2020-04-13T18:33:36Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/124594
dc.descriptionThesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 72-76).en_US
dc.description.abstractRecent developments in machine learning (ML) have persuaded researchers that automated technologies without human intervention may transform occupations across the economy. My research seeks to assess how and where ML will affect the workforce. I extend the ideas of Brynjolfsson, Mitchell, and Rock (2018), who assess each task in the economy for its Suitability for Machine Learning (SML). This paper builds on their summary statistics to provide a more detailed analysis of where ML is likely to have its greatest impact in the economy. Combining their technological suitability data with labor market data, this paper suggests a policy model for better planning labor mobility and allocation of human resources in the face of upcoming technological changes.en_US
dc.description.statementofresponsibilityby Hazal Mine Kansu.en_US
dc.format.extent76 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectTechnology and Policy Program.en_US
dc.titleArtificial intelligence impact on occupations and workforceen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Technology and Policyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentTechnology and Policy Programen_US
dc.identifier.oclc1149091931en_US
dc.description.collectionS.M.inTechnologyandPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Societyen_US
dspace.imported2020-04-13T18:33:07Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentESDen_US
mit.thesis.departmentIDSSen_US


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