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dc.contributor.author | Martínez-Plumed, Fernando | es_ES |
dc.contributor.author | Tolan, Songül | es_ES |
dc.contributor.author | Pesole, Annarosa | es_ES |
dc.contributor.author | Hernández-Orallo, José | es_ES |
dc.contributor.author | Fernández-Macías, Enrique | es_ES |
dc.contributor.author | Gómez, Emilia | es_ES |
dc.date.accessioned | 2021-12-13T07:07:32Z | |
dc.date.available | 2021-12-13T07:07:32Z | |
dc.date.issued | 2020-02-08 | es_ES |
dc.identifier.isbn | 978-1-4503-7110-0 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/178198 | |
dc.description.abstract | [EN] In this paper we present a setting for examining the relation between the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labour and AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples. | es_ES |
dc.description.sponsorship | This material is based upon work supported by the EU (FEDER), and the Spanish MINECO under grant RTI2018-094403-B-C3, the Generalitat Valenciana PROMETEO/2019/098. F. Martínez-Plumed was also supported by INCIBE (Ayudas para la excelencia de los equipos de investigación avanzada en ciberseguridad), the European Commission (JRC) HUMAINT project (CT-EX2018D335821-101), and UPV (PAID-06-18). J. H-Orallo is also funded by an FLI grant RFP2-152. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Association for Computing Machinery (ACM) | es_ES |
dc.relation.ispartof | AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Labour market | es_ES |
dc.subject | Tasks | es_ES |
dc.subject | AI intensity | es_ES |
dc.subject | AI impact | es_ES |
dc.subject | AI benchmarks | es_ES |
dc.subject | Simulation | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Does AI Qualify for the Job?: A Bidirectional Model Mapping Labour and AI Intensities | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.identifier.doi | 10.1145/3375627.3375831 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094403-B-C32/ES/RAZONAMIENTO FORMAL PARA TECNOLOGIAS FACILITADORAS Y EMERGENTES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/FLI//RFP2-152/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC//CT-EX2018D335821-101/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//PAID-06-18/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV-VIN//SP20180210//A collaboratory for the evaluation, comparison and classification of Artificial Intelligence/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement///PROMETEO%2F2019%2F098//DEEPTRUST/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Martínez-Plumed, F.; Tolan, S.; Pesole, A.; Hernández-Orallo, J.; Fernández-Macías, E.; Gómez, E. (2020). Does AI Qualify for the Job?: A Bidirectional Model Mapping Labour and AI Intensities. Association for Computing Machinery (ACM). 94-100. https://doi.org/10.1145/3375627.3375831 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | Third AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES 2020) | es_ES |
dc.relation.conferencedate | Febrero 07-08,2020 | es_ES |
dc.relation.conferenceplace | New York, USA | es_ES |
dc.relation.publisherversion | https://doi.org/10.1145/3375627.3375831 | es_ES |
dc.description.upvformatpinicio | 94 | es_ES |
dc.description.upvformatpfin | 100 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | S\431995 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | Future of Life Institute | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Instituto Nacional de Ciberseguridad | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |