Keeping AI Governable: The In-Between as an Adaptive Governance Architecture

Abstract

Develops the In-Between as an adaptive governance architecture for AI systems operating under uncertainty. The second, retitled edition connects alignment, AI Control, monitoring, law, and institutional calibration through a Trust Harness, the D/C/K diagnostic capacities, dynamic evidence, and minimal governance experiments. Version 2.1 adds Chapter 17, Holding the Holders, which turns the diagnostic upward toward states, laboratories, deployers, and other actors with unilateral power over model continuation, constitution, and access.

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