Related Experiment Video
Updated: Jun 13, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Supporting Trustworthy AI Through Machine Unlearning
Emmie Hine1,2,3, Claudio Novelli4,5, Mariarosaria Taddeo6,7
1Department of Legal Studies, University of Bologna, Via Zamboni, 27/29, 40121, Bologna, Italy. emmie.hine@yale.edu.
None:
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.
Related Concept Videos
Non-equilibrium in the Cell
Stereotype Content Model
Purposive Learning
Avoidance Learning and Learned Helplessness
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

