Mind the Gap: Robustness Risks in PII Detection Systems
Read the original on arXiv Machine Learning →The paper "Mind the Gap: Robustness Risks in PII Detection Systems" examines how modern PII detection methods—encoder-based NER, rule-based hybrid detection, and generative LLM extraction—fail when faced with realistic distribution shifts such as noisy, unstructured, and informal inputs. A stress‑test benchmark covering seven categories of natural shift reveals that each architecture degrades in distinct ways: encoder models struggle with unseen surface forms and boundary detection, rule‑based systems falter on non‑standard formats, and LLMs suffer from entity‑type confusion and generation instability. The authors argue that aggregate benchmark scores hide these deployment‑critical weaknesses and propose a hybrid detection pipeline with a QA‑driven feedback loop to iteratively mitigate risks, also releasing the benchmark for OOD‑aware evaluation.
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