arXiv:2502.05684v5 Announce Type: replace-cross Abstract: How can we effectively remove or ``unlearn'' undesirable information, such as specific features or the influence of individual data points, from a learning outcome while minimizing utility loss and ensuring rigorous guarantees? We introduce a unified mathematical framework based on information-theoretic regularization to address both data-point unlearning and feature unlearning. For data-point unlearning, we introduce the \emph{Marginal Unlearning Principle}, an auditable and provable framework. Moreover, we provide an information-theor
The proliferation of AI models and increasing regulatory scrutiny around data privacy and bias are driving a critical need for effective unlearning mechanisms.
This research provides a foundational mathematical framework for provably removing undesirable information from AI models, addressing key concerns for deployable AI.
The ability to audit and guarantee that specific data or features have been unlearned offers a pathway to more compliant and ethically sound AI systems.
- · AI developers
- · Data privacy regulators
- · Sectors with sensitive data
- · Auditing and compliance firms
- · Malicious actors exploiting data in models
- · Companies with opaque AI practices
Increased trust and adoption of AI systems due to improved data governance capabilities.
Development of new tools and services specifically for AI unlearning and auditing.
Potential for privacy-preserving federated learning paradigms that incorporate 'unlearn by design' principles.
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Read at arXiv cs.AI