AI in the Boardroom

Name: AI in the Boardroom | Preparing Leaders for Responsible Governance
Brand: Business Expert Press
Price: 17.49 EUR
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Preparing Leaders for Responsible Governance

Tom Petro(Autor*in)

Business Expert Press

Erschienen am 25. Februar 2025

266 Seiten

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Inhalt

Front cover
Half title
Title
Copyright
Description
Contents
Testimonials
Foreword
Preface
Acknowledgments
Introduction
CHAPTER 1 The Case for Boardroom Oversight and Engagement
The Impact of AI
Strategic Boardroom Leadership of AI
Performance
Strategy
Risk Management
Purpose
Board's Role in AI Governance
New Risk Frontiers to Be Governed
Inadequate AI Governance Is Costly
Boardroom AI Expertise
CHAPTER 2 An Introduction to AI
Laying a Foundation
A Framework for AI Governance
CHAPTER 3 AI's Decision-Making Paradigms-Probabilistic or Deterministic
Deterministic AI
Probabilistic AI
A Spectrum
CHAPTER 4 Deciphering AI Risks -What Directors Should Know
Bias and Discrimination
Hallucinations
Privacy and Security
Ethics
Safety and Reliability
Transparency and Explainability
Drift
Missing Data
Messy Data
Unintended Consequences
Social Impacts
CHAPTER 5 A Primer on Data and Data Governance
Elements of a Well-Developed Data Governance Framework
Data Quality and Consistency
Metadata Management
Data Security
Data Privacy
Data Access and Usage
Data Lifecycle Management
Data Stewardship
Commitment From the Top
Understanding Data Types
Data Quality
Training Datasets Matter
Synthetic Training and Validation Datasets
The Board's Role in Data Governance
CHAPTER 6 A Board Member's Guide to Core AI Techniques
Symbolic Learning: Bridging Human Cognition and AI
Key Board Risk Oversight Considerations for Symbolic Learning
Symbolic Learning Decision-Making Paradigm
Machine Learning: Unleashing the Power of Data
Supervised Learning: Guided Discovery
Key Board Risk Oversight Considerations for Supervised Machine Learning
Supervised Learning Decision-Making Paradigm
Unsupervised Learning: Unveiling Hidden Patterns
Key Board Risk Oversight Considerations for Unsupervised Learning
Unsupervised Learning Decision-Making Paradigm
Reinforcement Learning: Learning Through Trial and Error
Reinforcement Learning Decision-Making Paradigm
Deep Learning: Harnessing the Power of Artifcial Neural Networks
Key Board Risk Oversight Considerations for Deep Learning
Deep Learning Decision-Making Paradigm
Computer Vision: Seeing Through the Machine's Eye
Key Board Risk Oversight Considerations for Computer Vision
Computer Vision Decision-Making Paradigm
Sensor Fusion: The Future of Intelligent Perception
Key Board Risk Oversight Considerations for Sensor Fusion AI
Sensor Fusion Decision-Making Paradigm
Introducing Language-Based AI: Unlocking Language Intelligence
The Shift to Small Language Models
Generative AI Models
LLMs in Generative AI
Specialized Generative Models
Generative AI in Other Domains
Generative AI Versus LLMs and NLP
Natural Language Processing: Empowering Machines to Speak and Comprehend
Key Board Risk Oversight Issues for Natural Language Processing
Natural Processing Language Decision-Making Paradigm
Large Language Models: Harnessing the Power of Human Language
Key Board Risk Oversight Considerations for Large Language Models
Large Language Model Decision-Making Paradigm
Generative AI: Unleashing the Power of Creation
New Opportunities and Challenges
Key Board Risk Oversight Issues for Generative AI
Generative AI Model Decision-Making Paradigm
Ensemble Learning
Homogeneous Ensemble Learning
Key Board Risk Oversight Considerations for Homogeneous Ensembles
Multimodal Ensemble Learning
Key Board Risk Oversight Considerations for Multimodal Ensembles
Summary
CHAPTER 7 A Board Risk Management Framework for AI
Embracing RAI Governance Principles
Conduct a Readiness Assessment
Data Governance
Talent
Core System Fitness
Inventory of AI Uses
Market Analysis
Defensive Posture
Workforce and Cultural Readiness
Compliance Readiness
Integrate AI Into Governance Frameworks
Key Elements of AI Governance Framework
Policy Scope and Objectives
Business Strategy and Operating Plans
Roles and Responsibilities
Risk Appetite
Risk Assessments
Third-Party AI Systems
Data Management
AI Development Life Cycle
Model and Training Data Validation
Three Lines of Defense
Incident Response
Privacy and AI Regulatory Compliance
Assurance and AI Audits
Liability Insurance for AI
Oversight Strategy
Situationally Tailored Oversight
Cultivating AI Governance Expertise
Strategies for Cultivating AI Governance Expertise at the Board Level
Rethinking Committee Structures
Key Considerations for Committee Structures
Strategies for Building AI Expertise Within the Ranks of Management
Decisioning Framework
Tracking Mechanisms
Regular Progress Reports and Dashboards
Reporting and Feedback Mechanisms
Summary
CHAPTER 8 A Board Director Call to Action
Emerging Boardroom Considerations
Evolving Case Law and Regulation
Intellectual Property Considerations
Evolving Regulatory Landscape
Insurance and Liability
AI Audits and Standards
Security and Privacy Risks
The Changing Role of Boards in the AI Era
A Boardroom Imperative
Appendix A Board Oversight Checklist for AI
Appendix B Checklist for Third-Party AI Adoption
Risk Assessment and Mapping
Three Lines of Defense
Vendor Due Diligence and Contractual Safeguards
Monitoring and Auditing
Transparency and Explainability
Human Oversight and Accountability
Disclosure and Regulatory Compliance
Continuous Improvement and Learning
Appendix C Board Director Checklist for AI Data Governance
Data Inventory and Mapping
Data Quality and Integrity
Data Security and Privacy
Data Governance Framework
Data for AI Development and Deployment
Data Use in AI Models
Data Sharing and Collaboration
Board Oversight and Accountability
Independent Audits and Reviews
Appendix D Board Governance Checklist for Third-Party Data in AI
Data Sourcing and Due Diligence
Data Quality and Integrity
Data Use and Ethical Considerations
Third-Party Data for Proprietary Models
Third-Party Data for External Models
Contractual Safeguards for Third-Party Data
Regulatory Compliance and Risk Management
Board Oversight and Reporting
Appendix E Governance Questions for Core AI Techniques
Symbolic Learning
Supervised Learning
Unsupervised Learning
Reinforcement learning
Deep Learning
Computer Vision
Sensor Fusion
Natural Language Processing
Large Language Models
Generative AI
Homogeneous Ensemble Learning
Multimodal Ensemble Learning
Appendix F Root Causes of Bias in AI Models
Biased Data
Data Bias: Predictable Versus Masked
Biased Algorithms
Algorithmic Bias: Encoded Versus Distributed
Biased Metrics
Metrics Bias: Masked Accuracy Versus Misleading Uncertainty
Biased Deployment
Deployment Bias: Contextual Impact Versus Adaptive Interventions
Appendix G Five Prominent Homogeneous Ensemble Learning Models
Random Forest
XGBoost
Gradient Boosting Machine (GBM)
Stacked Ensemble
Bayesian Model Averaging (BMA)
Appendix H Five Prominent Multimodal Ensemble Learning Models
Multimodal Generative Models (e.g., GPT, DALL·E)
Vision-Language Models (e.g., CLIP, Flamingo)
Multimodal Transformers (e.g., ViLT, LXMERT)
Multimodal Variational Autoencoders
Multimodal Attention Networks (e.g., FLAVA)
Glossary of AI Terminology
References
About the Author
Index
OTHER TITLES IN THE CORPORATE GOVERNANCE COLLECTION
Concise and Applied Business Books
Back cover

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