arXiv:2506.11253v2 Announce Type: replace-cross Abstract: Machine unlearning removes certain training data points and their influence from AI models (e.g., when a data owner revokes their consent to allow models to learn from the data). In this position paper, we propose to lift data-tracing machine unlearning to knowledge-tracing for foundation models (FMs). We support this position based on practical needs and insights from cognitive studies. Practically, tracing data cannot meet the diverse unlearning requests for FMs, which may be from regulators, enterprise users, product teams, etc., who
The proliferation of foundation models and increasing concerns around data privacy, intellectual property, and regulatory compliance necessitate advanced unlearning mechanisms beyond simple data deletion.
This concept introduces a more sophisticated and potentially scalable approach to meeting diverse unlearning requests for large AI models, fundamentally impacting how FMs can be managed and regulated.
The focus shifts from simply tracing and removing specific data points to understanding and surgically removing 'knowledge' learned by foundation models, allowing for more granular control over model behavior and compliance.
- · AI model developers
- · Enterprises deploying FMs
- · Regulatory bodies
- · Data privacy advocates
- · Malicious data injectors
- · Models reliant on unverified data
- · Organizations with poor data governance
Foundation models gain enhanced capabilities for ethical design and regulatory compliance through precise knowledge removal.
New standards and tools emerge for measuring and verifying 'knowledge unlearning' in complex AI systems, fostering greater trust.
The ability to 'unlearn' specific knowledge could enable dynamic model adaptation, leading to more resilient and ethically configurable AI agents.
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Read at arXiv cs.LG