This commentary serves as a companion guide to the MCC-AI—model contractual clauses designed to help public sector entities procure AI systems in line with the upcoming requirements of the EU AI Act.
This template provides Australian government buyers with a modular set of model clauses for procuring AI systems or services involving AI. It emphasizes safety, transparency, fairness, and regulatory compliance, with practical tools to embed risk controls in contracts.
This guide introduces the Agent Name Service (ANS), a DNS-like system for secure AI agent discovery. Designed for agentic AI ecosystems, it lays out a framework for identity, interoperability, and trust through a protocol-agnostic registry, PKI, and JSON schemas.
A comprehensive and fully customizable organizational AI policy template designed for operationalizing responsible AI. Rooted in ISO/IEC 42001 and NIST AI RMF, it’s packed with detailed, actionable governance, risk, and lifecycle guidance.
IBM’s 2025 white paper offers a structured look at AI agents—what they are, what they do, and the risks they pose. It outlines benefits, emerging challenges, and a mitigation framework grounded in IBM’s AI governance practices, tooling, and research.
Reddit deception, retro machines, and a checklist built for real audits. This issue looks at what happens when AI literacy fails, what a serious audit tool actually looks like, and why Steve Jobs may have predicted ChatGPT—with a little help from Aristotle.
This is a comprehensive, activity-based workbook designed to help public sector teams embed accountability throughout the AI lifecycle. It breaks down accountability into practical components and provides a Process-Based Governance (PBG) framework with interactive tools and exercises.
This practical workbook demystifies AI safety through hands-on activities, real-world case studies, and clear guidance aligned with the SSAFE-D and CARE-ACT frameworks. It’s a powerful tool for embedding safety into every stage of AI development and deployment.
This workbook offers a hands-on framework for embedding responsible data stewardship into public sector AI projects. Combining foundational principles with practical templates and workshop activities, it helps teams manage data integrity, quality, and privacy across the AI lifecycle.
This two-part guide from the Alan Turing Institute provides a practical, workshop-ready framework for embedding sustainability into AI projects, grounded in ethics, human rights, and real-world harms. Perfect for public sector teams building responsible AI.
This facilitator-focused workbook offers a hands-on introduction to AI ethics and governance through a sociotechnical lens. Designed for public sector use, it combines foundational concepts with interactive activities to help embed responsible AI practices.
This guide breaks down the practical steps organizations should take to meet Article 4 AI literacy obligations under the EU AI Act. It’s a hands-on, strategy-rich resource for embedding AI understanding across stakeholders—from employees to affected users.
his report captures the results of an extensive stakeholder consultation run by the EU AI Office. It focuses on two controversial sections of the AI Act: the definition of an “AI system” and the interpretation of prohibited AI applications under Article 5.
A practical and timely Q&A document from the European Commission offering detailed guidance on Article 4 of the EU AI Act, which introduces a legal obligation for AI literacy.
A comprehensive audit framework developed by The IIA to help internal auditors assess and assure AI governance, risk, and control environments. Updated in 2024 to align with recent advances and standards like NIST AI RMF and large language model use.
A comprehensive, practitioner-friendly checklist covering 100+ AI audit questions across governance, ethics, security, bias, explainability, and compliance. It aligns with standards like ISO 42001, GDPR, and the EU AI Act, making it a strong operational guide for risk assessments.
Anthropic’s guide for enterprises adopting generative AI blends real-world deployment insights with a clear, staged strategy. From AI governance to technical scaling, it outlines how organizations can responsibly and effectively operationalize AI like Claude.
This updated 2025 guide offers a complete breakdown of the EU AI Act’s conformity assessment (CA) process for high-risk AI systems, aligned with the final adopted text. It clarifies when CAs are required, who performs them, and what legal, procedural, and technical requirements must be met.
This practical, executive-level guide helps business leaders and technologists reframe how they approach generative AI—shifting from mere users to true value creators.
This report takes the pulse of global boardrooms and reveals a growing awareness—but slow action—on AI governance. With only 5% of firms fully integrating AI into strategy, Deloitte urges boards to accelerate education, oversight, and alignment with strategic goals.
This expansive report draws on a new survey of 840 enterprises in G7 countries and 167 in Brazil to reveal how firms are using AI, where they struggle, and what kind of public support they value. It’s an evidence-rich resource for shaping practical AI policy.
A compact, practical guide to understanding the most influential cognitive biases in everyday thinking, decision-making, and AI design—plus a bonus chapter on algorithmic bias. A must-read for anyone working at the intersection of technology, governance, and ethics.
Simon Mylius’s Scalable AI Incident Classification showcases a proof-of-concept system using large language models (LLMs) to summarize, classify, and rate AI incidents at scale.
Amlan Mohanty’s report, Making AI Self-Regulation Work, offers a comprehensive framework for deploying self-regulation as a foundational piece of India’s AI governance.
Curated Library of AI Governance Resources
