Risk-Adaptive System and Method for Continuous Verification of Human-Machine Alignment Prior to Consequential Action
Year: 2026
Type: preprint
Abstract: This preprint discloses a risk-adaptive system and method for constructing, verifying, and continuously re-verifying a machine-readable representation of authorized human purpose prior to and during machine execution of consequential action. Existing authorization systems resolve who may access a resource. They do not resolve for what declared purpose, under what constraints, toward what success criterion, or whether that authorization remains valid as conditions change. The disclosed system addresses that gap. Core mechanisms: a twelve-primitive alignment state with immutable pre-declared success criteria; a bounded human-question budget in which machine-resolvable determinations do not consume human judgment; a six-level risk-to-assurance ladder from authenticated session through independent verification channels and continuous tether; drift detection across eight categories that invalidates stale authorization; domain-specific safe-state transition distinguished from shutdown; cryptographically signed purpose-bound authorization tokens portable across independent systems; and a structural prohibition on inferring authorization from physiological signal. A corresponding United States provisional patent application was filed on September 5, 2026 (Application No. 64/148,864). This disclosure is published openly as prior art. Published by Build Something.
Author: Finerty, Jonathan
Language: English
Topic: Access Control and Trust
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7208838722
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