{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/124594"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/124594","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Artificial intelligence impact on occupations and workforce","abstract":"Recent 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.","abstract_html":"Recent 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.","abstract_has_math":false,"creators":["Kansu, Hazal Mine."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Institute for Data, Systems, and Society","school":null,"contributors":[],"advisors":["Geoffrey G. Parker."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:15Z","subjects":["Institute for Data, Systems, and Society.","Technology and Policy Program."],"languages":["eng"],"rights":["MIT 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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/124594","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Geoffrey G. Parker."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society","ESD","Technology and Policy Program","IDSS","Massachusetts Institute of Technology. Engineering Systems Division"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","Technology and Policy Program."]},{"key":"dc:creator","label":"Author","values":["Kansu, Hazal Mine."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-04-13T18:33:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-04-13T18:33:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2019"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Institute for Data, Systems, and Society.","Technology and Policy Program."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["MIT 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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/124594"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 72-76)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Recent 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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Technology and Policy"]},{"key":"dc:title","label":"Title","values":["Artificial intelligence impact on occupations and workforce"]}]}],"canonical_facts":{"dc:contributor.advisor":["Geoffrey G. Parker."],"dc:contributor.department":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society","ESD","Technology and Policy Program","IDSS","Massachusetts Institute of Technology. Engineering Systems Division"],"dc:contributor.other":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","Technology and Policy Program."],"dc:creator":["Kansu, Hazal Mine."],"dc:date.accessioned":["2020-04-13T18:33:36Z"],"dc:date.available":["2020-04-13T18:33:36Z"],"dc:date.issued":["2019"],"dc:description":["Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 72-76)."],"dc:description.abstract":["Recent 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."],"dc:description.degree":["S.M. in Technology and Policy"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/124594"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT 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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Institute for Data, Systems, and Society.","Technology and Policy Program."],"dc:title":["Artificial intelligence impact on occupations and workforce"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:21:15Z"}