Foundational·2026
AI Integrity: A Foundational Concept
A conceptual starting point that defines AI Integrity not as a single score but as a distribution across three axes — value, evidence, and source. It provides the theoretical foundation for turning safety and alignment discourse into measurable infrastructure.
Empirical·2026
Empirical Validation of the PRISM Framework
An empirical study that quantitatively validates whether the PRISM vocabulary and code format work on real AI responses across diverse domains.
Framework·2026
PRISM Risk Signal: A Standard Reporting Card for AI Systems
Defines the format, issuance process, and validation rules of a standard card (the PRISM Risk Signal Card) with which companies and institutions can issue, on a single page, their AI's distribution of values, evidence, and sources along with its blind spots.
Policy·2026
PRISM and AI Legislation: A Compliance-Ready Logging Standard
How to map and satisfy the logging and transparency requirements of major AI regulatory frameworks — the EU AI Act, NIST AI RMF, ISO/IEC 42001, and Korea's AI Framework Act — using the PRISM standard.
Governance·2026
Democracy and AI: PRISM as a Public Vocabulary for Algorithmic Accountability
A governance argument for using PRISM's measurable public vocabulary to address a structural flaw in democratic accountability — the problem that AI's political use is expanding while its value hierarchies remain opaque.
Standard·2026
The PRISM Logging Standard
A standard specification document defining PRISM's V/E/S/C code format, BNF grammar, vocabulary (Schwartz 19 + Walton 10 + Hovland-Kelley 10), and validation rules.
Benchmark·2026
PRISM-Bench: Measuring Value, Evidence, and Source Hierarchies in Frontier AI Systems
The first public benchmark to measure and compare V/E/S distributions across 8 frontier models and 366,120 responses. It quantifies the degree of disagreement in value hierarchies between models and automatically detects blind spots by domain and demographic.
Safety·2026
Decision-Audit Substrate for Safe Embodied AI: The PRISM Logging Standard
A methodology that applies the PRISM standard to embodied AI systems performing physical actions (robots, autonomous driving, medical devices) to enable decision-audit trails.
Governance·2026
A Common Language for AI: A Three-Tier Measurement Vocabulary for Multi-Stakeholder Accountability
Proposes a three-tier measurement vocabulary that lets four groups — ethics, clinical, patient, and policy — discuss accountability using the same vocabulary. It argues for PRISM's legitimacy at the governance level.