Artificial Intelligence for Impact Assessment of Administrative Burdens

Detalhes bibliográficos
Autor(a) principal: Costa, Victor
Data de Publicação: 2024
Outros Autores: Coelho, Pedro, Castelli, Mauro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/164321
Resumo: Costa, V., Coelho, P., & Castelli, M. (2024). Artificial Intelligence for Impact Assessment of Administrative Burdens. Emerging Science Journal, 8(1), 270-282. https://doi.org/10.28991/ESJ-2024-08-01-019 --- This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the project UIDB/04152/2020-Centro de Investigação em Gestão de Informação – MagIC/NOVA IMS. This work was performed in the context of the the project “AI2A – Avaliação de Impacto e Inteligência Artificial” (POCI-05-5762-FSE-000226), funded by the program PORTUGAL 2020.
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spelling Artificial Intelligence for Impact Assessment of Administrative BurdensImpact AssessmentAdministrative BurdensArtificial IntelligenceNatural Language ProcessingTransformersBERTGeneralSDG 8 - Decent Work and Economic GrowthSDG 16 - Peace, Justice and Strong InstitutionsCosta, V., Coelho, P., & Castelli, M. (2024). Artificial Intelligence for Impact Assessment of Administrative Burdens. Emerging Science Journal, 8(1), 270-282. https://doi.org/10.28991/ESJ-2024-08-01-019 --- This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the project UIDB/04152/2020-Centro de Investigação em Gestão de Informação – MagIC/NOVA IMS. This work was performed in the context of the the project “AI2A – Avaliação de Impacto e Inteligência Artificial” (POCI-05-5762-FSE-000226), funded by the program PORTUGAL 2020.This study proposes the use of Artificial Intelligence (AI) to automatize part of the legislative impact assessment process. In particular, the focus of this study is the automatic identification of administrative burdens from legislative documents. The goal of impact assessment for administrative burdens is to apply an evidence-based approach toward compliance costs generated by regulation. Employing advanced Natural Language Processing (NLP) techniques based on a transformer architecture, a system was specifically developed and tested using Portuguese legislation. The experimental phase involved the system's ability to accurately and comprehensively identify administrative burdens. Experimental results demonstrated the system's effectiveness, showing its suitability for supporting the legislative impact assessment process by automating a time-consuming task. To the best of our knowledge, this is the first attempt concerning the use of AI for automatizing the identification of administrative burdens. The proposed system may provide governments and policymakers with a tool to speed up the legislative impact assessment process, thereby streamlining decision-making processes. Moreover, the use of AI can make the legislative impact assessment process less subjective, thus increasing its transparency and making citizens more confident about the impartiality of the process that leads to new legislation.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCosta, VictorCoelho, PedroCastelli, Mauro2024-03-01T00:28:00Z2024-02-012024-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/10362/164321eng2610-9182PURE: 84294875https://doi.org/10.28991/ESJ-2024-08-01-019info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:51:57Zoai:run.unl.pt:10362/164321Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:00:08.529809Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Artificial Intelligence for Impact Assessment of Administrative Burdens
title Artificial Intelligence for Impact Assessment of Administrative Burdens
spellingShingle Artificial Intelligence for Impact Assessment of Administrative Burdens
Costa, Victor
Impact Assessment
Administrative Burdens
Artificial Intelligence
Natural Language Processing
Transformers
BERT
General
SDG 8 - Decent Work and Economic Growth
SDG 16 - Peace, Justice and Strong Institutions
title_short Artificial Intelligence for Impact Assessment of Administrative Burdens
title_full Artificial Intelligence for Impact Assessment of Administrative Burdens
title_fullStr Artificial Intelligence for Impact Assessment of Administrative Burdens
title_full_unstemmed Artificial Intelligence for Impact Assessment of Administrative Burdens
title_sort Artificial Intelligence for Impact Assessment of Administrative Burdens
author Costa, Victor
author_facet Costa, Victor
Coelho, Pedro
Castelli, Mauro
author_role author
author2 Coelho, Pedro
Castelli, Mauro
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Costa, Victor
Coelho, Pedro
Castelli, Mauro
dc.subject.por.fl_str_mv Impact Assessment
Administrative Burdens
Artificial Intelligence
Natural Language Processing
Transformers
BERT
General
SDG 8 - Decent Work and Economic Growth
SDG 16 - Peace, Justice and Strong Institutions
topic Impact Assessment
Administrative Burdens
Artificial Intelligence
Natural Language Processing
Transformers
BERT
General
SDG 8 - Decent Work and Economic Growth
SDG 16 - Peace, Justice and Strong Institutions
description Costa, V., Coelho, P., & Castelli, M. (2024). Artificial Intelligence for Impact Assessment of Administrative Burdens. Emerging Science Journal, 8(1), 270-282. https://doi.org/10.28991/ESJ-2024-08-01-019 --- This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the project UIDB/04152/2020-Centro de Investigação em Gestão de Informação – MagIC/NOVA IMS. This work was performed in the context of the the project “AI2A – Avaliação de Impacto e Inteligência Artificial” (POCI-05-5762-FSE-000226), funded by the program PORTUGAL 2020.
publishDate 2024
dc.date.none.fl_str_mv 2024-03-01T00:28:00Z
2024-02-01
2024-02-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/164321
url http://hdl.handle.net/10362/164321
dc.language.iso.fl_str_mv eng
language eng
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PURE: 84294875
https://doi.org/10.28991/ESJ-2024-08-01-019
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eu_rights_str_mv openAccess
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