Artificial intelligence and job automation
: an EU analysis using online job vacancy dataAutor(es):
Pouliakas, Konstantinos
Centro Europeo para el Desarrollo de la Formación Profesional | Unión Europea
Series ; Cedefop working paperEditor: Luxembourg : Publications Office of the European Union, October 2021Descripción: 38 p. : gráf. ; 1 documento PDFTipo de contenido: texto (visual) Tipo de medio: electrónico
Tipo de soporte: recurso en líneaISBN: 978-92-896-3275-1Serie normalizada: Cedefop working paper
Tema(s): Empleo | Inteligencia Artificial | automatización | trabajo | IA | datos | oferta empleo en línea | UERecursos en línea: 
Acceso al documento Resumen: This study utilises a novel big data set based on online job advertisements – Cedefop’s Skills OVATE – with information on the skills and work activities required by EU employers. The data provide insight into the task profiles of detailed occupations faced with higher automation risk or those relying on alternative digital technologies (robots, computer software, AI). The paper explores suitable machine and deep learning models to test how well a parsimonious set of task indicators can predict occupational automatability. Work activities associated with greater occupational automation risk and robot exposure (e.g. inspecting equipment, performing physical activities), typically concentrated in routine or manual jobs, differ from those prominent in occupations with higher AI exposure (e.g. thinking creatively, evaluating standards).
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CDO
El Centro de Documentación del Observatorio Nacional de las Telecomunicaciones y de la Sociedad de la Información (CDO) os da la bienvenida al catálogo bibliográfico sobre recursos digitales en las materias de Tecnologías de la Información y telecomunicaciones, Servicios públicos digitales, Administración Electrónica y Economía digital.
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Colección digital | Acceso libre online | 1000020177029 |
This study utilises a novel big data set based on online job advertisements – Cedefop’s Skills OVATE – with information on the skills and work activities required by EU employers. The data provide insight into the task profiles of detailed occupations faced with higher automation risk or those relying on alternative digital technologies (robots, computer software, AI). The paper explores suitable machine and deep learning models to test how well a parsimonious set of task indicators can predict occupational automatability. Work activities associated with greater occupational automation risk and robot exposure (e.g. inspecting equipment, performing physical activities), typically concentrated in routine or manual jobs, differ from those prominent in occupations with higher AI exposure (e.g. thinking creatively, evaluating standards).
Creative Commons Attribution 4.0 International (CC BY 4.0)
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