Why Traditional Interpretability Tools Miss Edge Case Signals
You train a model, validate it, deploy it. Then a single unusual input—a rare disease code, an unexpected sensor reading, a user from an underrepresented demographic—causes a failure that none of your interpretability tools flagged. This is not a rare bug; it is a structural blind spot in how most popular explanation methods work. This guide explains why traditional interpretability tools miss edge case signals, compares three practical alternatives, and gives you a decision framework for choosing the right approach for your team. Who Needs to Decide — and Why Now If your team is responsible for a model that will encounter inputs far from the training distribution—and most production models do—you have already felt the gap.