What does this research mean for the field?

Internal intention in AI systems can be operationally identified and measured through a formal framework that incorporates constructive exclusion, statistical guarantees, and external validation. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The work aims to provide a comprehensive measurement and validation framework for internal intention in AI systems.

April 4, 2026Open Access

Operational Identifiability of Internal Intention in AI Systems — A Measurement and Validation Framework (v2.0)

Key Points

The work aims to provide a comprehensive measurement and validation framework for internal intention in AI systems.
Extends operationalization of internal intention into a robust measurement framework.
Introduces identifiability and worst-case bounds for reliable detection.
Applies statistical methods for validation, including multiple testing correction and UCB.
Utilizes information-theoretic thresholds for intervention coverage analysis.
Defines a detection core and measurement theory for operational effectiveness.
Proposes a clear framework for assessing internal intention with robustness against errors.
Establishes new metrics for identifying false negatives, enhancing detection accuracy.
Demonstrates the utility of independent proxy validation for external validation of constructs.

Abstract

This work extends the formal operationalization of internal intention in AI systems (v1, DOI: 10.5281/zenodo.19376962) into a full measurement and validation framework. Version 2.0 introduces: identifiability with constructive exclusion and worst-case bounds; robust statistical guarantees (UCB, independence, multiple testing correction); intervention coverage with information-theoretic thresholds; elicitability as an optimization problem; external validation via independent proxy triad; false negative characterization and detection boundary (α*). This document defines the detection core and measurement theory. Companion work addresses construct validity and necessity. This work is also subject to the ORIGÓ License (see document).

Operational Identifiability of Internal Intention in AI Systems — A Measurement and Validation Framework (v2.0)

Key Points

Abstract

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