CONCEPT
Precautionary Incrementalism
The modification of muddling through appropriate to domains where errors are irreversible — bolder constraints in the early iterations, because the cost of under-constraining exceeds the cost of over-constraining.
Precautionary incrementalism is the modification of the standard method appropriate to the boundary conditions where its assumptions strain. Standard incrementalism relies on error correction: try something, observe consequences, fix what went wrong. The method works when errors are reversible. It fails when they are not. Some AI governance decisions produce consequences that cannot be undone because the damage completes before the feedback arrives. For these high-stakes domains, precautionary incrementalism biases early iterations toward constraint rather than permissiveness, accepting suboptimal outcomes on some dimensions in exchange for preserving the option to do better later.
In The You On AI Field Guide
The domains where the modification is indicated are identifiable. Autonomous weapons systems that make lethal decisions faster than human deliberation can operate. AI in critical infrastructure — power grids, financial systems, communications networks — where system failures cascade at machine speed. The reshaping of children's cognitive development during windows of neurological plasticity that, once closed, do not reopen. In each domain, the standard reliance on error correction encounters