Principles of AI Governance and Model Risk Management

Master the Techniques for Ethical and Transparent AI Systems
1. Auflage 2024
ISBN: 979-8-8688-0982-8
Verlag: Apress

Buch, Englisch, 472 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 756 g

Navigate the complex landscape of Artificial Intelligence (AI) governance and model risk management using a holistic approach encompassing people, processes, and technology. This book provides practical guidance, oversight structure and centers of excellence, and actionable insights for organizations seeking to harness the power of AI responsibly, ethically, and transparently. By addressing the technical, ethical, and societal dimensions of AI governance, organizations will be empowered to build trustworthy AI systems that benefit both their bottom line and the broader community.

Featuring successful mitigating controls based on proven use cases, the book underscores the importance of aligning AI strategy with AI governance, striking a balance between AI innovation, risk mitigation as well as broader business goals. You’ll receive pointers for designing a well-governed AI development lifecycle, emphasizing transparency, accountability, and continuous monitoring throughout the AI development lifecycle. This book highlights the importance of collaboration between stakeholders, i.e., boards of directors, CxOs, corporate counsel, compliance officers, audit executives, data scientists, developers, validators, etc.

You’ll gain practical advice on addressing the challenges related to the ownership of AI-generated content and models, stressing the need for legal frameworks and international collaboration. You’ll also learn the importance of auditing AI systems, developing protocols for rapid response in case of AI-related crises, and building capacity for AI actors through education. Principles of AI Governance and Model Risk Management demonstrates its value-added uniqueness by detailing a strategy to ensure a cohesive approach to managing AI-related risks, global compliance, policy, privacy, and AI-human collaboration and oversight.

What You Will Learn

- Different approaches to AI adoption, from building in-house AI capabilities to partnering with external providers

- Key factors to consider when choosing an AI solution and how to ensure its successful integration into existing workflows

- AI technologies, their business impact, and ethical considerations to make informed decisions and foster responsible AI

- The environmental impacts of AI systems and the need for sustainable practices in AI development and deployment.

Who This Book is For

Business executives and process owners/representatives, risk officers, cybersecurity professionals, legal counsel and ethics officers, human resource professionals, data scientists, AI developers, and CTOs.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Sayles, James

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Wirtschaftswissenschaften Betriebswirtschaft Management Risikomanagement
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Risikobewertung, Risikotheorie

Inhaltsverzeichnis

Chapter 1 - The Current State of AI Governance and Model Risk Management.- Chapter 2 - AI Strategy and AI Governance Interoperability.- Chapter 3 - How to Sound Like an AI Governance Guru.- Chapter 4 - Designing a Well-Governed AI Lifecycle Model.- Chapter 5 - AI Governance for Trustworthy AI – The Governances in AI Governance.- Chapter 6 - Designing Your AI Governance Framework.- Chapter 7 - AI Governance and Oversight Model.- Chapter 8 - Managing and Addressing AI Compliance.- Chapter 9 - Integrating AI Governance with Enterprise Governance Risk and Compliance.- Chapter 10 - AI Policy Management and Enforcement.- Chapter 11 - Maintaining Privacy within your AI Governance Model.- Chapter 12 - Human Oversight of AI Systems.- Chapter 13 - The Power of Stakeholder Engagement in AI Governance.- Chapter 14 - Considering the Environmental Impacts of AI Systems.- Chapter 15 - Developing the Protocols for Rapid Response in Case of an AI-Related Crisis.- Chapter 16 - Capacity Building for AI Actors.- Chapter 17 - Intellectual Property Rights with AI Technologies.- Chapter 18 - Auditing AI Systems.- Chapter 19 - AI Model Inventory and Facts.- Chapter 20 - Ramesh.- Chapter 21 – AI Governance and SDLC Integration.- Chapter 22: AI Through the Lens of Non-Technical Business Leaders: Embracing AI with Caution.- Chapter 23: Navigating the AI Frontier with this Sales Bible: Sales and Marketing Strategies for AI Governance and Risk Management Solutions.

Über Autor(innen)

With over 25 years in executive leadership in AI Strategies, Governance and Model Risk Management, Data Governance, and Cybersecurity solutions, James is a certified AI Governance Strategist, Certified Chief Information Security Officer, and Enterprise Governance Risk and Compliance (GRC) Executive. He has a Big 4 tenure and has helped Fortune companies execute global AI/data governance and model risk management frameworks, eGRC ecosystems, and business-aligned cybersecurity strategies across industry diversification in the public and private sectors. He is a regulatory compliance/RegTech expert with unique skills in Financial Services and Healthcare and is passionate about leading the charge for ethical and trustworthy AI systems. Moreover, he has advised senior business executives and their board of directors on the necessary disciplines, thought leadership, and managerial responsibilities that are defined as good governance.

James is an entrepreneur at heart and takes pride in being an industry trailblazer and developing key business strategies. His career exhibits the ability to solve business problems at the intersection of business and technology. He’s founded and co-founded a few companies specializing in AI development, data governance and privacy, risk-governance strategies, enterprise governance, risk and compliance management, and cybersecurity. AI governance and risk management is a current focus. He continues to seek ways to automate business processes using AI.