PRACTICAL PROJECT MANAGEMENT

The book guides the reader through the auditing and compliance process of the newly released ISO Artificial Intelligence standard. It provides tools and best practices on how to put together an AI management system that is certifiable and sheds light on ethical and legal challenges business leaders struggle with to make their AI system comply with existing laws and regulations, and the ethical framework of the organization. The book is unique because it provides implementation guidance on the new certification and conformity assessment process required by the new ISO Standard on Artificial Intelligence (ISO 42001:2023 Artificial Intelligence Management System) published by ISO in August 2023. This is the first book that addresses this issue. As a member of the US/ISO team who participated in the drafting of this standard during the last 3 years, the author has direct knowledge and insights that are critical to the implementation of the standard. He explains the context of how to interpret ISO clauses, gives examples and guidelines, and provides best practices that help compliance managers and senior leadership understand how to put together the AI compliance system to certify their AI system. The reader will find in the book a complete guide to the certification process of AI systems and the conformity assessment required by the standard. It also provides guidance on how to read the new EU AI Act and some of the U.S. legislations, such as NYC Local Law 144, enacted in July 2023. This is the first book that helps the reader create an internal auditing program that enhances the company’s AI compliance framework. Generative AI has taken the world by storm, and currently, there is no international standard that provides guidance on how to put together a management system that helps business leaders address issues of AI governance, AI structure, AI risk, AI audit, and AI impact analysis. ISO/IEC 42001:2023 is the first international mandatory and certifiable standard that provides a comprehensive and well-integrated framework for the issue of AI governance. This book provides a step-by-step process on how to implement the standard so the AI system can pass the ISO accreditation process. Cover Half Title Title Page Copyright Page Table of Contents Foreword Preface Generative AI: The Promise, Risks, Challenges, and Opportunities Understanding Generative AI The Promise and Perils of Generative AI Responsible Development and Deployment The Generative AI Era Conclusion About the Author and the Contributors Introduction and Book Organization Part 1: Artificial Intelligence and Generative AI: Forces behind the Digital Transformation Chapter 1: Artificial Intelligence: A Transformational Technology Introduction Definition of AI Different Types of AI Artificial Narrow Intelligence (ANI) Artificial General Intelligence (AGI) Artificial Super Intelligence (ASI) Chapter 2: Generative AI: A “Spark from AGI” Introduction What Is Generative AI Generative AI Added Value and Economic Sectors Impacted Generative AI Harm, Risk, and Cost Emerging (and Unknown) Abilities Harmful Content Privacy and Data Protection Cybersecurity Threat Hallucinations Chapter 3: Economic Impact of Artificial Intelligence Introduction Economic Sectors That Will Be Impacted by AI The Manufacturing Sector The Finance Sector The Transportation Industry National Security and Law Enforcement Sector The Healthcare Sector The Cybersecurity Sector Strategy Implications: The Current State of AI Adoption AI in Automation: RPA AI in Prediction: Gaining Cognitive Insight AI and Cognitive Engagement: Enhancing Customer Relationship Management AI and Robotics Industrial Robotics and AI Medical Robots Military Robots and AI Impact of Automation on Society: How Will Society React to AI and Automation? Scenario One: Society Will Accept AI Scenario Two: Society Will Reject AI Scenario Three: Society Will Accept Automation The Jobs AI Will Create Trainer Explainer Sustainer Countries’ AI National Strategy Chapter 4: Digital Transformation: How to Prepare Your Organization for Change Introduction Digital Transformation Framework Leadership Commitment: Building Digital Leadership Reskilling and Upskilling Teach Critical Thinking Skills Teach Innovation Build a Customer-Centricity Capability Build an Enterprise Agility Self-Directed Team to Manage Collaboration Agile Process: Review Your Decision-Making Process Part 2: Artificial Intelligence Management System: How to Put in Place an AI Governance System Introduction Chapter 5: Clause 4: Context of the Organization Introduction: Why Context Analysis Is Crucial to AI Management System? What to Include in the Context Analysis Competitive Landscape and Stakeholders’ Analysis Legal Context Analysis: Laws and Regulations The General Data Protection Regulation The EU AI Act Unacceptable Risk High Risk Limited Risk Low Risk The US AI Regulatory Landscape The Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence Ethical AI: Responsible and Trustworthy AI Do No Harm Principle The Principle of Fairness and Non-Discrimination Human Oversight and Respect of Human Autonomy Principle The Principle of Explainability The Principle of Robustness ISO Certification Process: How to Conduct an Analysis of the Context Step 1: Mobilize the Team and Clarify the Mission Step 2: Set the Roadmap Step 3: Conduct Discovery Sessions Step 4: Start with the External Environment Step 5: Conduct an Internal Analysis Chapter 6: Clause 5: Leadership Introduction Set the Vision Set the Vision, Define the Priorities, and the Strategic Direction Lead with Responsible AI Principles (RAI) Set the Tone and Use Proactive Communication AI Policy: Characteristics and Components What Should Be in the AI Policy? A Statement on the Scope of the Policy, Its Purpose, and What the Policy Intends to Achieve Guidelines on the Use of AI in the Organization Show How AI Management System Integrates with Other Management Systems Define the Roles and Responsibilities Data and Privacy AI Compliance AI Talent Management Monitoring and Improvement Review and Alignments How Do You Create an AI Policy? Form the Team Engage with Stakeholders Conduct Discovery Sessions and Workshop Meetings with Different Stakeholders Review the Laws, Regulations, and Ethical Framework AI Strategy Step 1: Develop AI Use Case Enhancing Customer Satisfaction Agile and Data-Driven Decision-Making Process Creating Efficiencies Improving Productivity Step 2: Assess the Competitive Landscape Step 3: Reorganize Internally Build and Update the Current Technology Infrastructure to Empower the AI Management System Design a Data Strategy Talent Strategy AI Oversight: The Role of Board of Directors Chapter 7: Clause 6: Planning Introduction AI Risk Management, Risk Treatment, and Impact Assessment The Concept of Risk The Concept of Risk Assessment (Clause 6.1.2) The Concept of Risk Treatment (Clause 6.1.3) The Concept of Impact Assessment (Clause 6.1.4) A Typology of Risks Performance Risk Security Risk Enterprise Risk Reputational Risk Legal and Regulatory Risk AI Scalability Risk The Black Box Risk AI Risk Management Planning: Principles, Framework, and Process AI Risk Framework: A Requirement to Certification AI Risk Management Foundations AI Risk Should Be Integrated into the Enterprise Risk Management System Embrace a Wholistic Perspective Customize Your Approach Be Inclusive of Your Stakeholders’ View Adopt an Agile Mindset Spell Out Your Assumptions Pay Attention to the Cognitive Bias Learn and Improve The Planning of Data Management Risk: An Imperative to AI Management System ISO Standard Data Quality Requirements Data Collection Phase Data Preparation Phase Problem Framing Phase The Planning of Change: AI Management System Change Strategy Create a Sense of Urgency Build the Guiding Team Get the Right Vision Communicate for Buy-In Empower Teams Perseverance Chapter 8: Clause 7: Support Introduction Tangible Resources: The AI Infrastructure Computing Performance Storage Capacity Networking Infrastructure Security Intangible Resources: AI Competence Model What Is a Competence Model? AI-Focused Competence Model Competence Domain 1: Digital Planning and Design Model Competence Domain 2: Data Use and Governance Model Competence Domain 3: Digital Management and Execution Model Competence’s Attitudes Creativity Adaptability Experimentation Curiosity Trust Awareness (Section 7.3) All Employees Need to Be Aware of the AI Policy Governance and Leadership AI Scope and Objectives Use of Responsible AI AI Risks Data Usage How Employees Contribute to a Better Improved AI Management System Communication between Different AI Teams The Use of Data The Need to Reskills and Upskill Noncompliance Issues of the AI Management System Communication (Clause 7.4) Encourage Face-to-Face Communication The Medium Is the Message Create Policy Champions Documented Information Documented Information Required: What Needs to Be Documented Chapter 9: Clause 8: Operation Introduction AI Project Life Cycle Design Phase: Process Grouping 1 Identify the Problem Select the Idea Understand the Context of the Organization Conduct a Literature Review Frame the Question ISO/IEC 42001 Requirements for Process Grouping 1 (Design) Responsible AI Trustworthy AI Design Phase: Process Grouping 2 Data Collection Data Wrangling ISO/IEC 42001 Requirements for Process Grouping 2 (Design) Data Quality Data Resources Development Phase: Process Grouping 3 Build the Model Evaluate the Model ISO/IEC 42001 Requirements for Process Grouping 3 Deployment Phase: Process Grouping 4 Monitor Model Behavior Monitor KPIs ISO/IEC 42001 Requirements for Process Grouping 4 Chapter 10: Clause 9: Performance Evaluation Introduction AI Management System Evaluation and Assessment Requirements AI Management System Assessment and Audit The Scope of the Performance Evaluation Assessment Criteria, Metrics, KPIs Large Language Models Audit Set Up an Internal Audit Program Management Review Chapter 11: Clause 10: Improvement Introduction Corrective Actions and Preventive Actions Framework Corrective Actions Preventive Actions Continual Improvement: The PDCA Approach Conclusion Appendix: 50 Most Important Terms in AI and ISO Standards Bibliography Index