Why it matters
The human-in-the-loop paradigm promises automation's efficiency without sacrificing safety or nuance. But it hides an underexamined assumption: that human involvement produces genuine discernment, not just a rubber stamp. In practice, human-AI interaction often occurs in environments where throughput or business incent
My takeaway: Build AI Systems for Discernment, Not Approval - Angel Ortmann Lee, Duolingo is a model-evaluation signal. The practical read is to tie capability claims to evidence, launch criteria, and regression tests rather than relying on demos or benchmark headlines.