AI Risk Management Framework 1.0
Organizes AI risk management around Govern, Map, Measure, and Manage across the lifecycle.
NIST — official/source page ↗A claim–argument–evidence–counterargument–residual-risk template for demonstrating why a high-impact machine system is believed to respect defined constitutional constraints.
It is an adaptation of assurance thinking to rights-preserving machine governance: state the claim, show the argument, attach evidence, surface counterarguments and disclose residual risk. It is not certification by assertion.
Repository validators can establish that an implementation has a certain property under test. External governance sources can support principles. Production operation requires separate observed evidence. These categories should never be collapsed.
A model, policy, threshold or data-source update can invalidate prior assurance. Every case therefore attaches to versioned system and policy identifiers.
Source links establish traceability and support. They do not imply that the source endorses Concresca’s constitutional proposals.
Organizes AI risk management around Govern, Map, Measure, and Manage across the lifecycle.
NIST — official/source page ↗Provides a national AI assurance framework for Australian government use.
Australian Government — official/source page ↗Separates declaration, local demonstration, external verification, and operational evidence; provenance alone is not semantic truth.
MachineTradecraft.com — official/source page ↗NO JUDGMENT WHATSOEVER. Concresca coordinates without assigning moral worth, character, guilt, danger, trustworthiness, loyalty, purity, normality, or social standing. Questions, thoughts, identities, messages, content, and conduct are not objects of Concresca judgment.