What question did this study set out to answer?

This research aims to validate an external measurement instrument for AI governance in financial institutions through empirical methods.

May 1, 2026Open Access

The Stationary Sea: Measurement Instrument Validation for External Assessment of AI Governance in Regulated Financial Institutions

Key Points

This research aims to validate an external measurement instrument for AI governance in financial institutions through empirical methods.
Validated instrument used web-scale search alongside large language models for evidence extraction.
Conducted population-scale validation across 543 institutions, scanning AI agents and governance edges.
Tested three hypotheses regarding evidence recovery and instrument validity using classical measurement theory.
Total governance edges per AI agent remained stable at approximately 6.5 across multiple institutions.
Sector mean governance scores ranged from 12.10 for pension funds to 20.92 for banks, confirming known-groups validity.
The newer production scanner version (v13.1.0) recovered 21.7% more edges than the earlier version (v11.1).

Abstract

This paper reports the validation of an external measurement instrument for AI governance across regulated financial institutions. The instrument extracts governance topology from public evidence using large language models supplemented by web-scale search and a transparent three-tier evidence classification (Observed, Derived, Inferred). The paper documents a three-era scanner lineage (v11.1 baseline, v11.1.1 attribution overlay, v12.1.1 production scanner) extended by a v13 corrective iteration locked at SHA256 e5250de8e9de07d6, with cryptographic hashes at every pipeline stage, a four-source variance attribution framework, a capture-replay validation protocol, and a three-level cross-version comparability framework grounded in classical measurement theory (Cronbach and Meehl, 1955; Messick, 1995; Cronbach, Gleser, Nanda and Rajaratnam, 1972). Empirical validation is drawn from a population-scale campaign of 543 regulated institutions covering 95,876 AI agents and 626,390 governance edges across 66 countries. The 506-institution founding cohort was scanned 20 to 29 April 2026; the 36-institution expansion cohort was scanned 27 April 2026 under the same v13.1.0 scanner. Three findings bear on instrument validity: total edges per agent remain stable at 6.47 to 6.58 across eleven independent founding-cohort batches with the expansion cohort at 6.42; sector-level mean governance scores track the theoretical gradient of prudential supervisory maturity from pension funds (12.10) to banks (20.92), providing known-groups validity per Cronbach and Meehl (1955); cross-version comparison of v11.1 and v13.1.0 on 505 rescanned institutions shows v13.1.0 recovers 21.7 percent more observed edges in absolute terms while additionally capturing structural inference that v11.1 did not recover. Three hypotheses are tested and confirmed: that the production scanner version has reached diminishing returns in evidence recovery; that aggregate metrics demonstrate population-scale stability; and that the instrument satisfies known-groups validity in the classical measurement sense. JEL Classification: G21, G28, G38, O33, C18, C63, C81.

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