{"id":1341,"date":"2019-03-26T11:35:48","date_gmt":"2019-03-26T15:35:48","guid":{"rendered":"https:\/\/sites.bu.edu\/tpri\/?p=1341"},"modified":"2019-03-26T11:44:59","modified_gmt":"2019-03-26T15:44:59","slug":"toward-understanding-the-impact-of-artificial-intelligence-on-labor","status":"publish","type":"post","link":"https:\/\/sites.bu.edu\/tpri\/2019\/03\/26\/toward-understanding-the-impact-of-artificial-intelligence-on-labor\/","title":{"rendered":"Toward Understanding the Impact of Artificial Intelligence on Labor"},"content":{"rendered":"<p><span class=\"highwire-citation-author first has-tooltip hasTooltip\" data-delta=\"0\" data-hasqtip=\"2\" aria-describedby=\"qtip-2\">Morgan R. Frank<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author\" data-delta=\"1\">David Autor<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author\" data-delta=\"2\">James E. Bessen<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author\" data-delta=\"3\">Erik Brynjolfsson<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author\" data-delta=\"4\">Manuel Cebrian<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author has-tooltip hasTooltip\" data-delta=\"5\" data-hasqtip=\"3\">David J. Deming<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author has-tooltip hasTooltip\" data-delta=\"6\" data-hasqtip=\"0\" aria-describedby=\"qtip-0\">Maryann Feldman<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author\" data-delta=\"7\">Matthew Groh<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author\" data-delta=\"8\">Jos\u00e9 Lobo<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author\" data-delta=\"9\">Esteban Moro<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author\" data-delta=\"10\">Dashun Wang<\/span><span>,\u00a0<\/span><span class=\"highwire-citation-author has-tooltip hasTooltip\" data-delta=\"11\" data-hasqtip=\"4\">Hyejin Youn<\/span><span>, and\u00a0<\/span><span class=\"highwire-citation-author has-tooltip hasTooltip\" data-delta=\"12\" data-hasqtip=\"1\" aria-describedby=\"qtip-1\">Iyad Rahwan<\/span><\/p>\n<div class=\"section abstract\" id=\"abstract-1\">\n<p id=\"p-4\">Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. This\u00a0 paper discusses the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human\u2013machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, the paper recommends developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.<\/p>\n<\/div>\n<p><a href=\"https:\/\/www.pnas.org\/content\/early\/2019\/03\/21\/1900949116\">Paper in <em>Proceedings of the\u00a0National Academy of Sciences<\/em><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Morgan R. Frank,\u00a0David Autor,\u00a0James E. Bessen,\u00a0Erik Brynjolfsson, et al.<\/p>\n<p>New research analyzing the barriers that inhibit accurately measuring the effects of AI and automation on the future of work and developing a decision framework that focuses on resilience to unexpected scenarios.<\/p>\n","protected":false},"author":11401,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[16,20],"tags":[3,4,21,34,24,22,8,30,13],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/posts\/1341"}],"collection":[{"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/users\/11401"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/comments?post=1341"}],"version-history":[{"count":1,"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/posts\/1341\/revisions"}],"predecessor-version":[{"id":1342,"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/posts\/1341\/revisions\/1342"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/media?parent=1341"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/categories?post=1341"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.bu.edu\/tpri\/wp-json\/wp\/v2\/tags?post=1341"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}