Definition ∞ Class unlearning in machine learning refers to the process of selectively removing knowledge pertaining to a specific class of data from a trained model. This operation ensures that the model can no longer identify or generate outputs related to the expunged class. It is particularly relevant for privacy-preserving AI and data deletion requirements. The objective is to eliminate specific information without retraining the entire model.
Context ∞ While not directly a crypto term, class unlearning gains relevance in discussions about data privacy and compliance within blockchain-adjacent AI applications. News reports might cover its application in decentralized machine learning platforms or in systems handling sensitive user data. The debate often centers on the computational feasibility and verifiability of complete unlearning, especially in distributed environments. Future research focuses on efficient and cryptographically verifiable unlearning methods.
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