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Ciência da Informação
Article . 2024 . Peer-reviewed
License: CC BY SA
Data sources: Crossref
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ZENODO
Other literature type . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Presentation . 2024
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2024
License: CC BY
Data sources: Datacite
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Ciência Aberta e Inteligência Artificial

desafios éticos e transparência em modelos generativos
Authors: Rockembach, Moisés;

Ciência Aberta e Inteligência Artificial

Abstract

O artigo aborda os desafios éticos e de transparência associados ao uso de Inteligência Artificial (IA) em alinhamento com os princípios da Ciência Aberta, com um foco especial nos modelos generativos, como o ChatGPT e Mistral. Utilizando uma metodologia que combina pesquisa documental e bibliográfica, o estudo examina tanto modelos de IA abertos quanto fechados, analisando como essas tecnologias podem distorcer a integridade científica. O objetivo central é fomentar um debate sobre as implicações dessas distorções e propor mecanismos para mitigar tais efeitos, reforçando a necessidade de modelos transparentes que assegurem a reprodutibilidade científica. Conclui-se que, quando aplicados com rigor ético, os modelos generativos têm o potencial de auxiliar significativamente a Ciência Aberta, promovendo um novo nível de colaboração e inovação no campo científico.

Country
Portugal
Related Organizations
Keywords

ciência aberta, ética, inteligência artificial, transparência

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average