Artificial Intelligence Regulatory Uncertainty (AIRU): A Standard for Measuring Institutional Regulatory Uncertainty in AI Governance

Abstract

This document establishes the Artificial Intelligence Regulatory Uncertainty (AIRU) Standard, an open and version-controlled measurement standard for institutional uncertainty in AI governance. AIRU treats regulatory transitions—not document counts, keyword frequency, or regulatory strictness—as the basic observation unit. Each transition is anchored to an official source, linked to a regulatory chain, coded across uncertainty dimensions, and preserved in an auditable database. The first financial validation uses a preregistered event-study design on 20 US regulatory transitions grouped into 15 calendar clusters. A high-exposure basket (SOXX and AIQ) is compared with a low-exposure basket (SPY and QQQ), with XLP as a placebo. The confirmatory endpoint is the cluster-level high-minus-low standardized abnormal absolute residual volatility (SAARV) contrast. Its mean is 0.0036, with a one-sided cluster sign-flip pvalue of 0.491 and a 95% bootstrap interval of [-0.190, 0.205]. Pre-specified robustness checks do not support a positive directional claim. The evidence establishes a transparent first validation, not a confirmed financial effect. Long-horizon GARCH-MIDAS validation is deferred under the G6 Tier C power gate.

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