Comprehensive documentation of the PAICE framework, security practices, and research methodology.
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A framework for assessing collaboration capability, governance readiness, and risk in AI-assisted systems
Rationale, framework, and architecture behind PAICE — the evidence layer for AI governance, measuring observable behavior rather than self-report.
Privacy-Preserving Assessment with Cryptographic Integrity
A comprehensive guide to PAICE's privacy-first architecture, security measures, and compliance with global data protection regulations including GDPR, CCPA, and SOC 2.
A Behavioral Skill Framework for Human-AI Performance Improvement
Presented at ISPI 2026, this whitepaper maps People+AI collaboration measurement to established performance improvement frameworks from Gilbert, Mager, Rummler-Brache, Thalheimer, Phillips, and Brinkerhoff.
A Board-Level Business Case for Measuring AI-Collaboration Reliability
Executive brief making the financial case for measuring whether your people catch AI errors. Converts the invisible liability of AI-assisted work into a managed, board-reportable signal.
Why Privacy by Architecture Is What Makes Behavioral AI Measurement Adoptable
How PAICE's privacy-by-architecture answers the works-council, DPO, and labor-counsel objections that gate deployment of any behavioral AI measurement program in Europe.
Mapping Behavioral Assessment to NIST AI RMF, ISO/IEC 42001, and the EU AI Act
Maps PAICE's behavioral output to the specific clauses of NIST AI RMF, ISO/IEC 42001, and the EU AI Act that concern human oversight — so compliance officers have an audit-file artifact instead of training-completion records.
All PAICE whitepapers are available for free download. Each document represents our commitment to transparency in AI collaboration measurement and data privacy.
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