Principles of AI Governance and Model Risk Management

Principles of AI Governance and Model Risk Management
Author: James Sayles
Publisher:
Total Pages: 0
Release: 2024-12-11
Genre: Business & Economics
ISBN:
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This is a comprehensive playbook that addresses the need for responsible Artificial Intelligence (AI) systems. The book emphasizes that AI governance and model risk management are not merely technical concerns but a holistic approach encompassing people, processes, and technology. It delves into the current state of AI governance, highlighting the varying maturity levels across industries and the challenges organizations face in establishing effective AI strategies and governance frameworks. It even provides 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 and risk mitigation- aligned to broader business goals. It provides practical advice for designing a well-governed AI development lifecycle, emphasizing transparency, accountability, and continuous monitoring throughout the AI development lifecycle. This book emphasizes the importance of collaboration between stakeholders, i.e., boards of directors, CxOs, corporate counsel, compliance officers, audit executives, data scientists, developers, validators, etc. This book 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. It provides 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. Furthermore, the book addresses 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. It also explores the environmental impacts of AI systems and the need for sustainable practices in AI development and deployment. It's a comprehensive roadmap for navigating the complex landscape of AI governance and model risk management. It 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, this book empowers organizations to build trustworthy AI systems that benefit both their bottom line and the broader community. 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 Understand AI technologies, their business impact, and ethical considerations to make informed decisions and foster responsible AI 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, CTOs and more

Risk Modeling

Risk Modeling
Author: Terisa Roberts
Publisher: John Wiley & Sons
Total Pages: 214
Release: 2022-09-20
Genre: Business & Economics
ISBN: 111982494X
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A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance
Author: El Bachir Boukherouaa
Publisher: International Monetary Fund
Total Pages: 35
Release: 2021-10-22
Genre: Business & Economics
ISBN: 1589063953
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This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Trustworthy AI

Trustworthy AI
Author: Beena Ammanath
Publisher: John Wiley & Sons
Total Pages: 230
Release: 2022-03-15
Genre: Computers
ISBN: 1119867959
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An essential resource on artificial intelligence ethics for business leaders In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI. Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents: In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI.

Supply Chain Management and Corporate Governance

Supply Chain Management and Corporate Governance
Author: Catherine Xiaocui Lou
Publisher: Taylor & Francis
Total Pages: 286
Release: 2022-07-29
Genre: Business & Economics
ISBN: 100062059X
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Supply Chain Management and Corporate Governance: Artificial Intelligence, Game Theory and Robust Optimisation is the first innovative, comprehensive analysis and analytical robust optimisation modelling of the relationships between corporate governance principles and supply chain management for risk management and decision-making under uncertainty in supply chain operations. To avoid corporate failures and crises caused by agency problems and other external factors, effective corporate governance mechanisms are essential for efficient supply chain management. This book develops a new collaborative robust supply chain management and corporate governance (RSCMCG) model and framework that combines good corporate governance practices for risk management strategies and decision-making under uncertainty. This model is developed as a principal–agent game theory model, and it is digitalised and computed by Excel algorithms and spreadsheets as an artificial intelligence and machine-learning algorithm. The implementation of the RSCMCG model provides optimal supply chain solutions, corporate governance principles and risk management strategies for supporting the company to achieve long-term benefits in firm value and maximising shareholders’ interests and corporate performance while maintaining robustness in an uncertain environment. This book shows the latest state of knowledge on the topic and will be of interest to researchers, academics, practitioners, policymakers and advanced students in the areas of corporate governance, supply chain management, finance, strategy and risk management.

Interpretable Machine Learning

Interpretable Machine Learning
Author: Christoph Molnar
Publisher: Lulu.com
Total Pages: 320
Release: 2020
Genre: Artificial intelligence
ISBN: 0244768528
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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Risk Management Handbook

Risk Management Handbook
Author: Federal Aviation Administration
Publisher: Simon and Schuster
Total Pages: 112
Release: 2012-07-03
Genre: Transportation
ISBN: 1620874598
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Every day in the United States, over two million men, women, and children step onto an aircraft and place their lives in the hands of strangers. As anyone who has ever flown knows, modern flight offers unparalleled advantages in travel and freedom, but it also comes with grave responsibility and risk. For the first time in its history, the Federal Aviation Administration has put together a set of easy-to-understand guidelines and principles that will help pilots of any skill level minimize risk and maximize safety while in the air. The Risk Management Handbook offers full-color diagrams and illustrations to help students and pilots visualize the science of flight, while providing straightforward information on decision-making and the risk-management process.

AI Governance

AI Governance
Author: Darryl Carlton
Publisher:
Total Pages: 0
Release: 2024-05-08
Genre: Business & Economics
ISBN: 9781634624459
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A data model represents a precise information landscape, and there are different levels of modeling depending on the audience and the model's purpose. A conceptual data model (CDM) is the highest-level of modeling and is designed to capture business needs and help both business and IT professionals agree on a common set of terms and definitions. It is an extremely powerful data model and this video will not only explain the CDM, but also work with you on a hands-on exercise covering the five steps for creating a CDM. Learn why the CDM is so important and see several actual CDMs and learn how each was built. This video was recorded live during Steve Hoberman's Data Modeling Master Class. More on this 3-day data modeling course at stevehoberman.com.

Responsible AI in the Enterprise

Responsible AI in the Enterprise
Author: Adnan Masood
Publisher: Packt Publishing Ltd
Total Pages: 318
Release: 2023-07-31
Genre: Computers
ISBN: 1803249668
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Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn ethical AI principles, frameworks, and governance Understand the concepts of fairness assessment and bias mitigation Introduce explainable AI and transparency in your machine learning models Book DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations. By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.What you will learn Understand explainable AI fundamentals, underlying methods, and techniques Explore model governance, including building explainable, auditable, and interpretable machine learning models Use partial dependence plot, global feature summary, individual condition expectation, and feature interaction Build explainable models with global and local feature summary, and influence functions in practice Design and build explainable machine learning pipelines with transparency Discover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms Who this book is for This book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.

Generative AI Governance

Generative AI Governance
Author: Anand Vemula
Publisher: Independently Published
Total Pages: 0
Release: 2024-07-22
Genre: Computers
ISBN:
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Generative AI Governance: A Comprehensive Guide is a detailed exploration of the principles, frameworks, and practices essential for the ethical and responsible management of generative AI technologies. The book is structured into six parts, each addressing critical aspects of AI governance, from foundational concepts to real-world case studies. Part I: Understanding Generative AI provides an introduction to generative AI, covering its historical evolution, key technologies, and diverse applications. It also examines the economic and social impacts of generative AI, along with future trends and opportunities in this rapidly advancing field. Part II: Governance Frameworks delves into the principles of AI governance, including ethical foundations, transparency, accountability, and fairness. It reviews the global regulatory landscape, discussing international, regional, and national regulations, compliance requirements, and industry standards. The section also presents best practices in AI development and deployment, supported by case studies of effective governance. Part III: Risk Management focuses on identifying and assessing the various risks associated with generative AI. It outlines risk assessment frameworks, tools, and techniques for risk identification and mitigation. Additionally, it covers strategies for implementing risk controls, monitoring risks, and handling incidents through well-developed response plans. Part IV: Organizational Governance examines internal governance structures, defining roles and responsibilities, governance committees, and organizational policies. It highlights data governance, emphasizing data privacy, protection, quality, and lifecycle management. The section also discusses the establishment and functioning of ethical AI committees, providing case studies for illustration. Part V: Implementation and Monitoring offers a roadmap for implementing AI governance, integrating it into the AI lifecycle, and managing change. It describes continuous monitoring techniques, key performance indicators (KPIs), and auditing and reporting processes. This part also looks ahead to future directions in AI governance, exploring emerging trends, innovations, and preparation for future challenges. Part VI: Case Studies and Real-World Examples presents practical examples of successful AI governance models, lessons learned from failures, and sector-specific governance practices. These case studies provide valuable insights and concrete examples to guide organizations in developing their own governance frameworks. Generative AI Governance: A Comprehensive Guide equips readers with the knowledge and tools needed to navigate the complex landscape of AI governance, ensuring that generative AI technologies are developed and deployed responsibly and ethically.