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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Unlearning for Governance of Foundation Models

Machine Unlearning for Governance of Foundation Models

Liu, Sijia
Liu, Yang
Baracaldo, Nathalie

Hardcover

Series: Synthesis Lectures on Computer Vision

General ComputersProbability & StatisticsComputer Security

Currently unavailable to order

ISBN10: 3032172810
ISBN13: 9783032172815
Publisher: Springer
Published: May 19 2026
Pages: 264
Weight: 1.33
Height: 0.82 Width: 6.95 Depth: 9.68
Language: English
This book provides a systematic and in-depth introduction to machine unlearning (MU) for foundation models, framed through an optimization-model-data 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 model utility, resist recovery attacks, and remain computationally efficient. From the model perspective, the book examines how knowledge is distributed across layers and latent subspaces, motivating modular and localized unlearning. From the data perspective, the book explores forget-set construction, data attribution, corruption, and coresets as key drivers of reliable forgetting.

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