Supporting Trustworthy AI Through Machine Unlearning.
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 tra...
| Publicado en: | Science & Engineering Ethics Vol. 30; no. 5; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=179626446&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 179626446 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 13533452 GNI jtl: Science & Engineering Ethics issn: 13533452 maglogo: N pubinfo: dt: Oct2024 vid: 30 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 179626446 10.1007/s11948-024-00500-5 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P size: 701KB tig: atl: Supporting Trustworthy AI Through Machine Unlearning. aug: au: Hine, Emmie Novelli, Claudio Taddeo, Mariarosaria Floridi, Luciano affil: https://ror.org/01111rn36 Department of Legal Studies, University of Bologna, Via Zamboni, 27/29, 40121, Bologna, Italy https://ror.org/05f950310 Centre for IT & IP Law, KU Leuven, Sint-Michielsstraat 6, 3000, Leuven, Flanders, Belgium https://ror.org/03v76x132 Digital Ethics Center, Yale University, 85 Trumbull St., 06511, New Haven, CT, USA https://ror.org/052gg0110 Oxford Internet Institute, University of Oxford, 1. St. Giles’, OX1 3JS, Oxford, UK The Alan Turing Institute, British Library, 96 Euston Rd, NW1 2DB, London, UK sug: keyword: GELSI Machine learning Machine unlearning Technology policy Trustworthy AI ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Science & Engineering Ethics is a copyright of Springer, 2024. All Rights Reserved. item: Science & Engineering Ethics holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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