What question did this study set out to answer?

The aim is to establish a standardized framework for quantifying and verifying AI decision-making authorization in real time.

April 3, 2026Open Access

Authority Confidence Rating (ACR): A Cryptographic Standard for Real-Time AI Decision Authorization Verification and AI Liability Quantification

Puntos clave

The aim is to establish a standardized framework for quantifying and verifying AI decision-making authorization in real time.
Introduced a base formula for calculating ACR based on verifiable human authorization.
Defined four certification tiers for AI decision processes.
Identified three application domains: insurance, regulatory compliance, and defense operations.
Relied on cryptographic enforcement data instead of traditional data collection methods.
Established ACR as a measurable standard for AI decision authorization.
Developed ACR variants highlighting different authorization risks and characteristics.
Outlined specific cases where unauthorized execution is structurally prevented with ACR-Zero.

Resumen

This paper introduces the Authority Confidence Rating (ACR), a cryptographic standard for quantifying AI decision authorization in real time. ACR measures the proportion of AI-influenced decisions that carried verifiable, cryptographically signed human authorization before execution. The standard defines a base formula (ACR = (V/D) × 100), a risk-weighted variant (ACR-W), four certification tiers (Platinum, Gold, Silver, Unrated), and a separate ACR-Zero designation for architectures where unauthorized execution is structurally impossible. Three application domains are identified: insurance underwriting, regulatory compliance under the EU AI Act and Product Liability Directive 2024/2853, and defense operations including real-time mission authorization monitoring. ACR is derived from cryptographic enforcement data, not from logs, surveys, or self-assessments. The framework is published under CC BY-NC-ND 4.0. Implementation of ACR computation using cryptographic authority enforcement may require licensing from YIN Technologies. This paper constitutes the first version of the ACR standard. An extended version introducing the Authority Confidence Deficit (ACD) and AI Liability Quantification (ALQ) constructs is published as a companion record. Implementation using cryptographic authority enforcement require licensing from the inventor. Licensing inquiries: ilyesmazari@hotmail.com

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