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.

Emmie Hine, C.N. (2024). Supporting Trustworthy AI Through Machine Unlearning. SCIENCE AND ENGINEERING ETHICS, 30(43), 1-13 [10.1007/s11948-024-00500-5].

Supporting Trustworthy AI Through Machine Unlearning

Emmie Hine
Primo
;
Claudio Novelli
Secondo
;
Luciano Floridi
Ultimo
2024

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.
2024
Emmie Hine, C.N. (2024). Supporting Trustworthy AI Through Machine Unlearning. SCIENCE AND ENGINEERING ETHICS, 30(43), 1-13 [10.1007/s11948-024-00500-5].
Emmie Hine, Claudio Novelli, MariaRosaria Taddeo, Luciano Floridi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/986134
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