Compliance

The “right to be forgotten” (GDPR Art. 17) has always seemed like kryptonite for large language models. Once a trillion-parameter system memorizes personal data, how can it truly be erased without starting training from scratch? Most prior attempts—whether using influence functions or alignment-style fine-tuning—felt like damage control: approximate, unverifiable, and too fragile to withstand regulatory scrutiny. This new paper, Unlearning at Scale, turns the problem on its head. It argues that forgetting is not a mathematical optimization problem, but a systems engineering challenge. If training can be made deterministic and auditable, then unlearning can be handled with the same rigor as database recovery or transaction rollbacks. ...

TL;DR A new multi‑agent pipeline builds an ontology‑light knowledge graph from regulatory text, embeds subject–predicate–object triplets alongside their source snippets in one vector store, and uses triplet‑level retrieval to ground LLM answers. The result: better section retrieval at stricter similarity thresholds, slightly higher answer accuracy, and far stronger navigability across related rules. For compliance teams, the payoff is auditability and explainability baked into the data layer, not just the prompt. ...

Machine unlearning, once a fringe technical curiosity, is fast becoming a legal and ethical imperative. With increasing regulatory demands like the GDPR’s “right to be forgotten,” AI developers are being asked a hard question: Can a large language model truly forget? A new paper from researchers at TUM and Mila provides an unexpectedly elegant answer. Instead of fighting model collapse—the phenomenon where iterative finetuning on synthetic data causes a model to forget—they propose embracing it. ...