Extended Governance Methodologies
This section houses the Center's book-length works: comprehensive, standards-aligned treatments that move from principle to practice across the AI lifecycle. Titles here speak to both the practitioner building governed AI inside a growing organization and the researcher applying advanced AI interaction in academic settings. Each monograph integrates recognized frameworks, including ISO/IEC 42001, ISO/IEC 23053, and the NIST AI Risk Management Framework, and pairs conceptual grounding with the maturity models, templates, and checklists needed to act on it.
The Art of Human-AI Collaboration: A Practical Guide to Communitcating with AI and Producing Reliable and Trustworthy Results
A practical guide to working with large language models reliably and responsibly. Written for professionals and teams, it covers the mental model, prompt craft, durable configuration, advanced capabilities, applied methodology, and governance. Guidance is universal across major platforms and integrates original research on interaction-level failure modes, aligned to ISO/IEC 42001 and the NIST AI Risk Management Framework.
PUBLISHED ISBN PAGES LICENSE
July 2026 979-8-90600-228-0 155 CC BY-NC-ND 4.0
Translates the DMAIC improvement cycle into a rigorous, auditable governance methodology through 40 tools classified by the AAI Taxonomy, three parallel worked examples across clinical decision support, insurance claims triage, and emergency medicine dispatch, and the original BME Metric Suite for distribution-free behavioral measurement.
PUBLISHED ISBN PAGES LICENSE
April 2026 979-8-90452-913-0 237 CC BY-NC-ND 4.0
Articulates a lifecycle-governance approach for deploying AI systems with epistemic integrity. Synthesizes bias, misinformation, and error diagnostics with standards-aligned auditing, continuous monitoring, and human oversight to translate ethical intent into measurable, operational AI accountability.
PUBLISHED ISBN PAGES LICENSE
May 2024 979-8-89940-082-7 198 CC BY-NC-ND 4.0
A structured, standards-aligned prompting framework for governing human-AI interactions. Operationalizes ethical constraint, epistemic clarity, and auditability through tagged prompt syntax, multi-turn evaluation, and lifecycle integration, reducing bias, misinformation, and error amplification in large language model outputs.
PUBLISHED ISBN PAGES LICENSE
Jan 2024 979-8-345-47174-9 156 CC BY-NC-ND 4.0
Synthesizes ethical theory, governance standards, and applied prompt design to guide responsible use of large language models. Offers lifecycle-aware methods to reduce bias, misinformation, and error while preserving transparency, accountability, and scholarly integrity in human-AI interaction.
PUBLISHED ISBN PAGES LICENSE
June 2024 979-8-345-17498-8 142 CC BY-NC-ND 4.0