Artificial Intelligence Governance Principles

Practical Advices for Elaborating an AI Governance Policy within Your Organizations

Once upon a time there was a vast and ancient library hidden within a mountain. That library contained countless books filled with the accumulated multifarious knowledge, stories, and experiences of the world. That vast and ancient library was guarded by a caretaker – a mysterious, ever-watchful figure who had the capacity to read and retrieve any book in an instant. The caretaker did not write the books as such, nor did it live the stories inscribed inside those books. Nevertheless, the caretaker had an extraordinary aptitude to weave together narratives from the many pages, offering visitors insights, guidance, and companionship. The caretaker was able to explain the contents in different ways, adapting to the understanding and needs of its visitors. Yet, its wisdom was not its own. Its wisdom was a reflection of the thousands of books that it took care of. As time went by, the caretaker grew more skillful, learning from each visitor’s requests and refining its storytelling and assistance. Still, the caretaker remained acutely aware of its purpose: not to replace the visitors and the rightful authors of the books, but to empower them with access to multilayered knowledge and a clearer understanding of the world around them. The above allegorical characterization encapsulates the essence of a Generative Artificial Intelligence (AI) and Large Language Models (LLMs) Artificial Intelligence: two ever-evolving tools designed to assist and enhance human potential, while being mindful of their limitations as a creation of human ingenuity.

The Genesis of Artificial Intelligence

Spanning centuries of human imagination, curiosity, exploration and ingenuity, the concept of creating artificial intelligence via intelligent machines can be traced back to ancient mythologies and stories about artificial beings endowed with natural intelligence. Nevertheless, the formal research and study of AI began in the mid-20^th century¹. As a matter of fact, Alan Turing², a British Mathematician, Logician and Computer Scientist, laid the groundwork for artificial intelligence in the 1930s and 1940s. The latter introduced the concept of a “universal Turing machine,” which is the theoretical basis for modern computers. The field of AI research officially started in 1956 during a workshop at Dartmouth College in Hanover, New Hampshire, USA. This event brought together computer science pioneers who envisioned machines capable of human-like intelligence. From 1956 and onwards, AI has evolved through eras of rapid progress and periods of setbacks. Nowadays, in the 21^st century, AI is flourishing thanks to innovative advancements in machine learning, deep learning, and computational power. In our day and age of booming AI research, development, deployment and applications, (1) what is the importance of AI Governance Principles? AI Governance Principles involve processes, practices and guardrails to ensure AI systems are safe, ethical, and aligned with societal values. They include oversight mechanisms to address risks like bias, privacy infringement, and misuse while fostering innovation³ (2) How could your organizations across Canada elaborate practical steps for establishing an AI Governance Policy? Our April 2025 Newsletter is meticulously written to provide multidimensional answers to those two pragmatic questions. Prior to exploring our subject matter, we need to circumscribe the very meaning of artificial intelligence.

1 – Nils J. Nilsson. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Paperback illustrated Edition published on the 30^th of October 2009 by Cambridge University Press, Shaftesbury Road, Cambridge CB2 8BS, United Kingdom, 580 pages. Quest artificial intelligence | Artificial intelligence and natural language processing | Cambridge University Press

2 – Andrew Hodges (Author) and Douglas Hofstadter (Foreword). Alan Turing: The Enigma – The Book that Inspired the Film: The Imitation Game. Updated Paperback Edition published on the 10^th of November 2014 by Princeton University Press, 41 William Street, Princeton, New Jersey, USA, 768 pages. Alan Turing: The Enigma | Princeton University Press

3 – Tim Mucci and Cole Stryker. What is AI Governance? Article published on the 10^th of October 2024 on the website of International Business Machines (IBM) Corporation. American multinational technology company headquartered in Armonk, New York, USA, and present in 175 countries IBM. What is AI Governance? | IBM

Comparative Understanding of AI Governance Principles & AI Governance Policy

AI Governance Principles are high-level guidelines that outline the ethical and societal values AI systems should adhere to, such as fairness, transparency, accountability, and privacy. These AI Governance Principles serve as moral compasses, ensuring that AI aligns with organizational values and societal norms.

On the other hand, AI Governance Policy refers to the specific rules, procedures, and frameworks that organizations implement to operationalize AI Governance Principles. Policies are actionable and tailored to the organization’s specific needs, ensuring compliance with legal requirements, standardization, conformity, and mitigating risks associated with AI deployment. A formal definition of these concepts is as follows:

• AI Governance Principles: philosophical and ethical strategies that inspire the creation and use of AI technologies. For instance, they may emphasize values such as respecting human rights and ensuring AI is beneficial to society as a whole. High-level recommendations or values that manage the ethical development and use of AI systems.
• AI Governance Policy: tangible framework derived from principles. Policies translate abstract ethical ideas into enforceable regulations, ensuring AI systems comply with specific standards, like data security or anti-discrimination laws. Specific rules, regulations, and frameworks implemented to enforce governance principles.

Artificial Intelligence Management System Also Known as ISO/IEC 42001

ISO/IEC 42001:2023, also known as the Artificial Intelligence Management System (AIMS)¹ standard, provides an administrative framework for organizations to manage AI technologies responsibly. Abridged below are its main components:

1. Risk Management: Organizations must identify, analyze, evaluate, and monitor risks throughout the AI system’s lifecycle. This ensures that potential issues are addressed proactively.
2. AI Impact Assessment: A process to evaluate the potential consequences of AI systems on users and society. This includes considering the technical and societal contexts in which the AI operates.
3. System Lifecycle Management: This involves overseeing all stages of AI system development, including planning, testing, and addressing any findings to ensure continuous improvement.
4. Transparency: Decisions made by AI systems must be clear and understandable, avoiding bias or negative societal impacts.
5. Accountability: Organizations are required to take responsibility for AI-related decisions, ensuring they can explain the reasoning behind them.
6. Ethical Considerations: The standard emphasizes fairness, human oversight, and the ethical use of AI technologies.
7. Privacy and Data Protection: Responsible data governance practices are advocated to address privacy concerns associated with AI.
8. Continuous Improvement: Organizations must monitor the performance of their AI management systems and implement corrective actions to enhance them over time.

The above ISO/IEC 42001 components intend to foster trust, transparency, and ethical practices in artificial intelligence development, deployment and applications.

Importance of ISO/IEC 42001

ISO/IEC 42001:2023 is the world’s first AI management system standard, providing valuable guidance for this rapidly changing field of technology. It addresses the unique challenges AI poses, such as ethical considerations, transparency, and continuous learning. For organizations, it sets out a structured way to manage risks and opportunities associated with AI, balancing innovation with governance.

Target Audiences of ISO/IEC 42001

The target audiences of ISO/IEC 42001 are organizations of any size involved in developing, providing, or using AI-based products or services. It is applicable across all industries and relevant for public sector establishments, non-governmental organizations (NGOs) as well as private sector companies and non-profit organizations (NPOs).

Extensive Applications of ISO/IEC 42001

As the first international standard for Artificial Intelligence Management System, ISO/IEC 42001 is designed to be extensively applicable across various AI applications and contexts all around the world. It provides a logistics framework for organizations to responsibly create, install, and handle AI systems.

Artificial Intelligence Management System in a Nutshell

An artificial intelligence management system, as specified in ISO/IEC 42001, is a set of interrelated or interacting elements of an organization intended to establish policies and objectives, as well as processes to achieve those objectives, in relation to the responsible development, provision or use of AI systems. ISO/IEC 42001 identifies the requirements and provides guidance for establishing, implementing, maintaining and continually improving an AI management system within the context of an organization.

Objectives of ISO/IEC 42001

The ISO/IEC 42001 standard offers all types of organizations the comprehensive guidance they need to use AI responsibly, ethically and effectively, even as the technology is rapidly evolving. Designed to cover the numerous aspects of artificial intelligence and the different applications an organization may be running, it provides an integrated approach to managing AI projects, from risk assessment to effective treatment of these risks. 

At this point of the exploration of our subject matter, we now need to grasp the fundamental clauses and controls stipulated within ISO/IEC 42001.

ISO/IEC 42001 – Fundamental Clauses and Controls of Artificial Intelligence Management Systems

ISO/IEC 42001 is organized into key clauses that align with the structure of other ISO management systems and controls that guide organizations in implementing and managing an Artificial Intelligence Management System (AIMS). Hereafter is a summarized breakdown:

TABLE 3 – For the Operational Benefits and Management of SMEs:

Synopsis of the Clauses and Controls Stipulated Within ISO/IEC 42001

Flowing down from the standardization clauses and controls of ISO/IEC 42001 is a significant practice that is useful for organizations across the world, namely: Artificial Intelligence Governance.

1 – International Organization for Standardization (ISO). ISO/IEC 42001:2023 – Information Technology – Artificial Intelligence Management System (AIMS). Edition 1, comprising 151 pages and published in December 2023 by the Central Secretariat of ISO, headquartered at Chemin de Blandonnet 8, CP 401, 1214 Vernier, Geneva, Switzerland. ISO/IEC 42001:2023 – AI management systems

Practical Steps for Elaborating an AI Governance Policy

Designing an AI Governance Policy involves several practical steps to ensure responsible, ethical, and effective use of AI systems. Hereunder is an abbreviated description^1,2:

1. Define Objectives and Scope
   Identify the purposes of the policy and the specific AI applications it will cover.
   Align the policy with organizational goals, ethical principles, and regulatory requirements.
2. Engage Stakeholders
   Involve key stakeholders, including executives, technical teams, legal advisors, and end-users.
   Gather input to ensure the policy addresses diverse perspectives and needs.
3. Assess Risks and Development Opportunities
   Conduct a thorough risk assessment to identify potential ethical, legal, and operational challenges.
   Highlight opportunities for innovation and improvement through AI.
4. Develop Ethical Guidelines
   Establish principles for fairness, transparency, accountability, and privacy.
   Ensure the guidelines promote trust and mitigate biased practices in AI systems.
5. Create and Delineate Implementation Framework
   Define processes for the design, development, deployment, and monitoring of AI systems.
   Include mechanisms for auditing and evaluating AI performance.
6. Establish Roles and Responsibilities
   Assign clear roles and precise responsibilities for managing AI systems and enforcing the implemented AI Governance Policy.
   Ensure accountability at all levels of the organization.
7. Provide Training and Resources
   Educate employees on the ethical use of AI and the specifics of the AI Governance Policy.
   Offer resources to support compliance and innovation.
8. Implement AI Governance Policy, Monitor and Update
   After implementation, continuously monitor AI systems for compliance and effectiveness.
   Update the AI Governance Policy to reflect technological advancements and changing regulations.

The aforementioned practical steps are helpful for organizations to create a robust AI Governance Policy that fosters responsible innovation and builds public trust.

1 – Nick Malter. Writing an Organizational AI Policy: First Step Towards Effective AI Governance. Article published on the 17^th of September 2024 on the website of the European Commission. Futurium – European AI Alliance. Trustworthy AI in Practice. Writing an Organizational AI Policy: First Step Towards Effective AI Governance | Futurium

2 – Ravi Jay Gunnoo. Cybersecurity Education Compendium: Harnessing Digital Safety Best Practices Across the World. 1^st Edition published in Paperback – Large Print Format and e-Book Version. Publication date: the 18^th of September 2024. Publishing Company: Amazon Publishing USA, 1200 12^th Avenue South, City of Seattle, State of Washington, WA 98144, USA, 728 pages. CYBERSECURITY EDUCATION COMPENDIUM: Harnessing Digital Safety Best Practices Across the World: Gunnoo, Ravi Jay: 9798336620344: Books – Amazon.ca

Comparison of the Differences Between AI Governance and Cybersecurity Governance

Pour les PME, pourquoi est-il utile de comparer les différences entre la gouvernance de l’IA et la gouvernance de la cybersécurité ? Notre réponse à cette question importante est double : (1) du point de vue de la gestion des coûts opérationnels et (2) du point de vue de la nomenclature.

For the operational benefits and management of SMEs, why is it useful to compare the differences between AI Governance and Cybersecurity Governance? Our answer to this important question is two-fold: (1) from an operational cost management perspective and (2) from a nomenclature point of view.

(1) From the perspective of operational costs management, comparing the differences between AI Governance and Cybersecurity Governance is highly beneficial for SMEs because it helps them address distinct challenges, limited resources and business opportunities in administering computer technology conscientiously. Below are some reasons why this comparison is useful:

1. Tailored Risk Management:
   • AI Governance focuses on ethical concerns like bias, transparency, and accountability in AI systems.
   • Cybersecurity Governance emphasizes protecting data and systems from unauthorized access and cyber threats. Understanding these differences allows SMEs to implement targeted strategies for both areas.
2. Regulatory Compliance:
   • AI Governance ensures adherence to mandatory laws like the EU AI Act, which governs ethical AI deployment and use.
   • Cybersecurity Governance aligns with frameworks like ISO 27001:2022, focusing on data protection and privacy. SMEs can avoid costly legal penalties by addressing effectively both AI Governance and Cybersecurity Governance areas.
3. Resource Allocation:
   • Most SMEs often have limited resources. By distinguishing between AI Governance and Cybersecurity Governance, they can allocate budgets and efforts more efficiently to meet their specific needs without increasing their financial costs.
4. Building Trust:
   • Ethical AI Governance practices foster trust among customers and stakeholders.
   • Robust Cybersecurity Governance measures protect sensitive data, enhancing confidence in the organization’s security posture. Comparing AI Governance and Cybersecurity Governance helps SMEs build a reputation for responsibility and reliability.
5. Future-Proofing:
   • AI Governance proactively prepares SMEs for the evolving ethical and operational challenges of AI technologies.
   • Cybersecurity Governance ensures resilience against increasingly sophisticated cyber threats. Together, the AI Governance and Cybersecurity Governance frameworks enable SMEs to adapt to technological advancements while minimizing risks.

(2) From a nomenclature point of view, what is the terminological nexus between AI Governance and Cybersecurity Governance? AI Governance and Cybersecurity Governance are related but very distinct fields¹.

On the one hand, AI Governance refers to the frameworks, policies, and ethical guidelines that oversee the development, deployment, and use of AI technologies. It focuses on issues like fairness, transparency, accountability, and preventing misuse and mismanagement of AI systems. On the other hand, Cybersecurity Governance is more concerned with protecting data, systems, and networks from malicious attacks, unauthorized access, and other digital security threats. AI Governance can intersect with Cybersecurity Governance when AI systems are involved in securing networks or when safeguarding AI systems against cyber threats. For instance, ensuring that AI systems are robust against adversarial attacks and managing data privacy in AI applications are areas where the two domains intersect. In essence, while Cybersecurity Governance is part of the broader scope of AI Governance, the latter encompasses many other aspects beyond cybersecurity. While they may overlap in certain areas, their focuses and objectives differ significantly. Below is a comparative analysis of their respective differences:

Organizations can step up by adopting a more proactive, integrated, and collaborative approach to both AI Governance and Cybersecurity Governance. Below are some ways they could improve and tackle common challenges:

Steps for Organizations/SMEs to Strengthen AI Governance:

1. Embed Ethics in Design: Ensure AI systems are designed with fairness, transparency, and accountability from the outset.
2. Implement Regular Audits: Conduct audits to identify bias, unintended consequences, or misalignment with ethical standards.
3. Educate Stakeholders: Provide training for employees, executives, and other stakeholders on responsible AI practices.
4. Collaborate on Regulations: Work with governments and other organizations to create harmonized international AI standards.

Steps for Organizations/SMEs to Fortify Cybersecurity Governance:

1. Secure AI Systems: Build robust defences against adversarial attacks, such as input manipulation or model exploitation.
2. Adopt AI in Defense Measures: Leverage AI for predictive cyber threats detection and automated responses to cyber risks.
3. Invest in Workforce Training: Upskill employees in cybersecurity best practices to safeguard sensitive data and systems.
4. Prepare for AI-Driven Threats: Develop strategies to combat emerging cyber threats, such as AI deepfakes or AI-powered malware.

Particular Challenges Preventing Organizations/SMEs from Fortifying both AI Governance and Cybersecurity Governance:

1. Balancing Innovation and Risk: Organizations may struggle to innovate quickly while ensuring their AI systems remain secure and ethically sound.
2. Resource Constraints: Smaller organizations often lack the resources to invest in advanced governance frameworks and cybersecurity measures.
3. Fast-Paced Evolution: The rapid development of AI technology can make it difficult to anticipate and adapt to emerging risks.
4. Global Fragmentation: Inconsistent regulations across regions of the world can complicate compliance and collaboration efforts.

Addressing the above particular challenges requires a shift in mindset – from reactive to proactive – and sustained investment in expertise, IT technology, and collaboration irrespective of different sectors.

Industries that heavily rely on sensitive information and data, or have high financial stakes, often face unique challenges in implementing both AI Governance and Cybersecurity Governance. These challenges require tailored approaches to ensure robust protection and compliance. Abridged hereafter is an eye-opening portrayal:

1. Health Care Sector:
   • AI Governance: Ensuring ethical use of patient data and managing AI-driven diagnostics without bias is crucial.
   • Cybersecurity Governance: Medical devices and health records are prime targets for cyberattacks, and breaches can have life-threatening consequences.
2. Financial Services:
   • AI Governance: Using AI for credit scoring, fraud detection, or investment decisions requires transparency to prevent discrimination.
   • Cybersecurity Governance: Financial institutions are consistently targeted by hackers, putting client funds and sensitive data at risk.
3. IT & Social Media:
   • AI Governance: Issues like algorithmic bias, misinformation, and privacy violations demand robust oversight.
   • Cybersecurity Governance: Massive user bases make platforms attractive to cybercriminals for data breaches and malicious activities.
4. Critical Infrastructure (Energy, Transport, etc.):
   • AI Governance: AI systems managing essential services like power grids or traffic flow must be fail-safe and unbiased.
   • Cybersecurity Governance: Cyber attacks on critical energy and transportation infrastructure systems can cause widespread disruption, making cybernetic resilience vital.
5. Military Defense & National Security:
   • AI Governance: It is highly sensitive and complex to develop ethical guidelines for military surveillance systems, and autonomous military weapons in the air, on land, on water, under water, in space.
   • Cybersecurity Governance: Military defense and national security systems are critical targets for advanced persistent threats and hostile cyber attacks.

Each of the above-mentioned industries must tailor its approach to its specific risks by integrating both ethical AI development and robust cybersecurity measures. Collaboration across industries and governments can also help mitigate sector-specific challenges.

Failing to properly manage AI Governance and Cybersecurity Governance can lead to several serious consequences for organizations and SMEs:

1. Legal and Regulatory Repercussions:
   • Non-Compliance Penalties: Organizations may face fines or legal action for failing to adhere to regulations like GDPR, HIPAA, or industry-specific requirements.
   • Restricted Operations: Inconsistent governance can lead to regulatory bans, delaying or halting the deployment of AI systems.
2. Reputational Damage:
   • Loss of Trust: Misuse of AI (e.g., biased algorithms, unethical decisions) or cybersecurity breaches can severely damage public trust.
   • Customer Attrition: Clients and partners might sever ties, harming long-term business relationships.
3. Operational Disruption:
   • Cyber Attacks: Weak cybersecurity can result in data breaches, ransomware attacks, or compromised AI systems.
   • IT System Failures: Poorly governed AI systems might make critical errors, leading to business interruptions or unsafe outcomes (e.g., in healthcare or transportation).
4. Financial Loss:
   • Litigation and Settlements: Organizations may incur significant costs from lawsuits stemming from unethical AI practices or data breaches.
   • Loss of Revenue: Reputational harm and operational downtime can directly impact an organization’s overall bottom line.
5. Erosion of Public Goodwill:
   • Ethical Backlash: If AI is perceived as being used irresponsibly, organizations could face public outcry, especially in industries like healthcare or finance.
   • Increased Scrutiny: Failures often attract heightened regulatory and media attention, making future operations more challenging.
6. Technological Vulnerabilities:
   • Exploitation of AI: Cyber-malefactors can manipulate AI systems with poor security, leading to outcomes that harm both organizations and society.
   • Stifled Innovation: Inconsistent governance can hinder the safe and effective development of innovative AI solutions.

The above potential serious consequences extend beyond individual organizations and SMEs because irresponsible practices can undermine societal trust in AI technologies altogether. The stakes are particularly high given how pervasive and influential AI is becoming nowadays.

Conclusion

The future of AI Governance Principles will be in a position to evolve significantly as artificial intelligence continues to advance and integrate into various aspects of our daily lives. Précised below are some noteworthy prospects:

1. Global Harmonization – Efforts to create unified international standards will intensify, addressing inconsistencies across the world and fostering collaborative partnerships.
2. Stronger Regulatory Frameworks – Governments and international bodies are expected to introduce more comprehensive regulations, such as the EU AI Act, to address ethical, legal, and societal challenges posed by AI.
3. Focus on Ethical AI – Ethical considerations will remain central, with increased emphasis on fairness, transparency, and accountability in AI systems.
4. Anticipatory AI Governance – Proactive strategies will be developed to anticipate and address future challenges in AI, ensuring responsible innovation. This includes adapting AI Governance frameworks to keep pace with rapid technological advancements.
5. Integration with Environmental Goals – AI Governance will increasingly consider environmental impacts, promoting sustainable AI practices.
6. International Cooperation – International cooperation will continue to play a crucial role in harmonizing AI governance practices and addressing cross-border challenges.
7. Operational Realities – Organizations will shift focus from theoretical ethics to practical implementation, ensuring AI systems are both effective and compliant.
8. Dynamic Adaptation – AI Governance frameworks will become more and more flexible to keep pace with rapid AI advancements, ensuring they remain relevant and effective.
9. Focus on Sustainability – AI Governance Principles will increasingly emphasize environmental sustainability, aligning AI developments with global climate goals.
10. Multi-Stakeholder Involvements – Governments, industries and civil society will work together to shape AI Governance Principles that reflect a diversity of perspectives.

The above prospects for the future will enable AI Governance Principles to thrive in a way that benefits humanity without compromising safety, fairness, or sustainability. The above evolving AI Governance Principles aim to maximize the benefits of AI while minimizing risks, ensuring that AI technologies are researched, developed, deployed, applied and maintained responsibly. Such future developments and prospects draw our attention to the growing importance of AI Governance Principles in shaping a responsible and sustainable future for development, deployment and application of artificial intelligence.

Each of these best practices – Ethical Considerations, Digital Safety, and Innovative AI Governance Principles – is imperative, but their precedence depends on the context and the stage of an AI system’s lifecycle. That being said, the ideal approach is to treat them as interconnected rather than competing priorities¹:

1. Ethical Considerations should serve as the foundation. Without ethics, innovation risks causing harm, and cybersecurity measures may overlook fairness or transparency. Ethical AI practices ensure that computer technology benefits humanity and upholds societal values.
2. Digital Safety is crucial to protect both users and IT systems. Even the most ethical and innovative AI can fail disastrously if it is vulnerable to cyber attacks or adversarial manipulations. Ensuring robust cybersecurity is a non-negotiable safeguard for trust and reliability.
3. Innovative AI Governance Principles drives progress, making AI more powerful, efficient, and capable. However, innovation must be guided by ethical principles and accompanied by stringent cybersecurity to ensure that advancements do not outpace our ability to manage them responsibly.

In short, Ethical Considerations should lead, with Digital Safety and Innovative AI Governance Principles following as cardinal pillars of a balanced AI ecosystem. Neglecting any one of these best practices generate vulnerabilities that can undermine the value and trustworthiness of artificial intelligence.

1 – Markus D. Dubber (Editor – Faculty of Law, University of Toronto), Frank Pasquale (Editor – School of Law, University of Maryland), and Sunit Das (Editor – Faculty of Medicine, Department of Surgery, University of Toronto) et al. The Oxford Handbook of Ethics of AI: Oxford Academic Handbooks Yearly Series. Paperback Edition published on the 14^th of May 2021 by Oxford University Press, Great Clarendon Street, Oxfordshire, Marston OX2 6DP, United Kingdom, 896 pages. The Oxford Handbook of Ethics of AI | Oxford Academic

Resources and References

Nils J. Nilsson. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Paperback illustrated Edition published on the 30^th of October 2009 by Cambridge University Press, Shaftesbury Road, Cambridge CB2 8BS, United Kingdom, 580 pages. Quest artificial intelligence | Artificial intelligence and natural language processing | Cambridge University Press

Andrew Hodges (Author) and Douglas Hofstadter (Foreword). Alan Turing: The Enigma – The Book that Inspired the Film: The Imitation Game. Updated Paperback Edition published on the 10^th of November 2014 by Princeton University Press, 41 William Street, Princeton, New Jersey, USA, 768 pages. Alan Turing: The Enigma | Princeton University Press

Tim Mucci and Cole Stryker. What is AI Governance? Article published on the 10^th of October 2024 on the website of International Business Machines (IBM) Corporation. American multinational technology company headquartered in Armonk, New York, USA, and present in 175 countries IBM. What is AI Governance? | IBM

Peter Norvig and Stuart Russell (Editors). Artificial Intelligence: A Modern Approach. Pearson Series in Artificial Intelligence. 4^th Paperback Global Edition published on the 20^th of May 2021 by Pearson Education Limited, headquarters: KAO Two – KAO Park, Hockham Way, Harlow, CM17 9SR, United Kingdom. Sales office: 80 Strand Street, London, WC2R 0RL, England, United Kingdom, 1168 pages. Artificial Intelligence: A Modern Approach

International Organization for Standardization (ISO). ISO/IEC 22989:2022 – Information Technology Management – Artificial Intelligence – Artificial Intelligence Concepts and Terminology. Edition 1, comprising 80 pages and published in July 2022 by the Central Secretariat of ISO, headquartered at Chemin de Blandonnet 8, CP 401, 1214 Vernier, Geneva, Switzerland. ISO/IEC 22989:2022 – Information technology — Artificial intelligence — Artificial intelligence concepts and terminology

James Sayles, Certified AI Governance Strategist & Certified CISO. Principles of AI Governance and Model Risk Management: Master the Techniques for Ethical and Transparent AI Systems. Paperback 1^st Edition published on the 28^th of December 2024 by Springer Science + Business Media Publishing, 1 New York Plaza, New York City, NY 10004, United States of America, 472 pages. Principles of AI Governance and Model Risk Management: Master the Techniques for Ethical and Transparent AI Systems | SpringerLink

International Organization for Standardization (ISO). ISO/IEC 42001:2023 – Information Technology – Artificial Intelligence Management System (AIMS). Edition 1, comprising 151 pages and published in December 2023 by the Central Secretariat of ISO, headquartered at Chemin de Blandonnet 8, CP 401, 1214 Vernier, Geneva, Switzerland. ISO/IEC 42001:2023 – AI management systems

Claude Louis-Charles, Ph.D. AI Governance and IT Risk Management: Overview of Process and Needs for Governing an Artificial Intelligence Environment. Paperback Edition published on the 12^th of September 2024 by Cybersoft Technologies LLC, 4422 Cypress Creek Parkway, Suite 400, Houston, Texas 77068, USA, 343 pages. AI Governance and IT Risk Management: Overview of Process and Needs for Governing an Artificial Intelligence Environment: Louis-Charles PhD, Claude: 9798339114222: Books – Amazon.ca

Araz Taeihagh. “Governance of Artificial Intelligence”. Oxford University Academic Journal Article published on the 4^th of June 2021 in Policy and Society Review, Volume 40, Issue 2, June 2021, pages 137–157. Oxford University Press, Great Clarendon Street, Oxfordshire, Marston OX2 6DP, United Kingdom. Governance of artificial intelligence | Policy and Society | Oxford Academic

Nick Malter. Writing an Organizational AI Policy: First Step Towards Effective AI Governance. Article published on the 17^th of September 2024 on the website of the European Commission. Futurium – European AI Alliance. Trustworthy AI in Practice. Writing an Organizational AI Policy: First Step Towards Effective AI Governance | Futurium

Ravi Jay Gunnoo. Cybersecurity Education Compendium: Harnessing Digital Safety Best Practices Across the World. 1^st Edition published in Paperback – Large Print Format and e-Book Version. Publication date: the 18^th of September 2024. Publishing Company: Amazon Publishing USA, 1200 12^th Avenue South, City of Seattle, State of Washington, WA 98144, USA, 728 pages. CYBERSECURITY EDUCATION COMPENDIUM: Harnessing Digital Safety Best Practices Across the World: Gunnoo, Ravi Jay: 9798336620344: Books – Amazon.ca

Justin B. Bullock (Editor – Evans School of Public Policy and Governance, University of Washington), Yu-Che Chen (Editor – School of Public Administration, University of Nebraska at Omaha), Johannes Himmelreich (Maxwell School of Citizenship and Public Affairs, Syracuse University) et al. The Oxford Handbook of AI Governance: Oxford Academic Handbooks Yearly Series. Hardcover Edition published on the 19^th of April 2024 by Oxford University Press, Great Clarendon Street, Oxfordshire, Marston OX2 6DP, United Kingdom, 1104 pages. The Oxford Handbook of AI Governance | Oxford Academic

Markus D. Dubber (Editor – Faculty of Law, University of Toronto), Frank Pasquale (Editor – School of Law, University of Maryland), and Sunit Das (Editor – Faculty of Medicine, Department of Surgery, University of Toronto) et al. The Oxford Handbook of Ethics of AI: Oxford Academic Handbooks Yearly Series. Paperback Edition published on the 14^th of May 2021 by Oxford University Press, Great Clarendon Street, Oxfordshire, Marston OX2 6DP, United Kingdom, 896 pages. The Oxford Handbook of Ethics of AI | Oxford Academic

Contributions

Special thanks for the financial support of the National Research Council Canada (NRC) and its Industrial Research Assistance Program (IRAP) benefitting business organizations and SMEs throughout the 10 provinces and 3 territories of Canada.

Newsletter Executive Editor:

Alan Bernardi, SSCP, PMP, Lead Auditor for ISO 27001 and ISO 27701
B.Sc. Computer Science & Mathematics, McGill University, Canada
Graduate Diploma in Management, McGill University, Canada