Machine Unlearning for Governance of Foundation ModelsAuthor(s): Sijia Liu, Yang Liu, Nathalie Baracaldo\nFormat: Hardback\nPublisher: Springer Nature Switzerland AG, Switzerland\nImprint: Springer Nature Switzerland AG\nISBN-13: 9783032172815, 978-3032172815\nSynopsis\nThis book provides a systematic and in-depth introduction to machine unlearning (MU) for foundation models, framed through an optimizationmodeldata tri-design perspective and complemented by assessments and applications. As foundation models are continuously adapted and reused, the ability to selectively remove unwanted data, knowledge, or model behavior, without full retraining, poses new theoretical and practical challenges. Thus, MU has become a critical capability for trustworthy, deployable, and regulation-ready artificial intelligence. From the optimization viewpoint, this book treats unlearning as a multi-objective and often adversarial problem that must simultaneously enforce targeted forgetting, preserve mode.