Implicit vs Explicit Behavioral Theory: Why Regulated AI Cannot Afford Black-Box Assumptions
Every machine-learning system for regulated decisioning encodes behavioral assumptions about the humans it acts on. In most stacks those assumptions are invisible. We argue that in high-stakes regulated contexts, invisibility is no longer an acceptable design choice.