Additional information
by James Sayles (Author)
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.
Back Jacket
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.
Author Biography
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.
