AI deployment should be bounded by what's actually been evaluated (evidence-bounded claims) and by hard constraints that no favorable results can override (measurement-bounded governance), requiring both better engineering and institutional repair.
This paper argues that AI governance frameworks like the EU AI Act and NIST AI RMF must go beyond principles to address institutional failures.