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Supporting Trustworthy AI Through Machine Unlearning.
Hine, Emmie; Novelli, Claudio; Taddeo, Mariarosaria; Floridi, Luciano.
Afiliación
  • Hine E; Department of Legal Studies, University of Bologna, Via Zamboni, 27/29, 40121, Bologna, Italy. emmie.hine@yale.edu.
  • Novelli C; Centre for IT & IP Law, KU Leuven, Sint-Michielsstraat 6, 3000, Leuven, Flanders, Belgium. emmie.hine@yale.edu.
  • Taddeo M; Digital Ethics Center, Yale University, 85 Trumbull St., New Haven, CT, 06511, USA. emmie.hine@yale.edu.
  • Floridi L; Department of Legal Studies, University of Bologna, Via Zamboni, 27/29, 40121, Bologna, Italy.
Sci Eng Ethics ; 30(5): 43, 2024 Sep 11.
Article en En | MEDLINE | ID: mdl-39259362
ABSTRACT
Machine unlearning (MU) is often analyzed in terms of how it can facilitate the "right to be forgotten." In this commentary, we show that MU can support the OECD's five principles for trustworthy AI, which are influencing AI development and regulation worldwide. This makes it a promising tool to translate AI principles into practice. We also argue that the implementation of MU is not without ethical risks. To address these concerns and amplify the positive impact of MU, we offer policy recommendations across six categories to encourage the research and uptake of this potentially highly influential new technology.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Confianza Límite: Humans Idioma: En Revista: Sci Eng Ethics Asunto de la revista: ETICA Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Confianza Límite: Humans Idioma: En Revista: Sci Eng Ethics Asunto de la revista: ETICA Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido