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008 211129s2021 lu d|||| |||| 00| 0 eng d
020 _a978-92-896-3275-1
022 _21831-2403
024 _d.
_2doi
_a10.2801/305373
035 _aTI-BA-21-004-EN-N
040 _aES-MaONT
100 2 _95317
_aPouliakas, Konstantinos
245 1 0 _aArtificial intelligence and job automation
_b: an EU analysis using online job vacancy data
_c/ K. Pouliakas, Cedefop
260 _aLuxembourg :
_bPublications Office of the European Union,
_cOctober 2021
300 _a38 p.
_b: gráf.
_c; 1 documento PDF
336 _atexto (visual)
_2isbdcontent
337 _aelectrónico
_2isbdmedia
338 _arecurso en línea
_2rdacarrier
490 _vCedefop working paper
_x; 6
520 _aThis 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).
540 _aCreative Commons Attribution 4.0 International (CC BY 4.0)
650 0 _aEmpleo
_95190
650 0 _aInteligencia Artificial
_94348
653 _aautomatización
653 _atrabajo
653 _aIA
653 _adatos
653 _aoferta empleo en línea
653 _aUE
710 2 _94954
_aCentro Europeo para el Desarrollo de la Formación Profesional
710 2 _aUnión Europea
_91508
830 _95318
_aCedefop working paper
856 4 _uhttps://op.europa.eu/en/publication-detail/-/publication/c840dd78-4b4a-11ec-91ac-01aa75ed71a1/language-en
_x0
_yAcceso al documento
942 _2z
_cINF
999 _c6835
_d6835