AI in the Boardroom : Preparing Leaders for Responsible Governance.

Format:

Book

Author/Creator:

Petro, Tom.

Language:

English

Physical Description:

1 online resource (206 pages)

Edition:

1st ed.

Place of Publication:

New York : Business Expert Press, 2025.

Summary:

Why Every Corporate Leader Needs This Book to Master AI Governance AI is reshaping industries faster than most leadership teams can adapt. Companies with strong digital and AI capabilities are outperforming peers by two to six times in Total Shareholder Return. The gap is widening as leaders compound their advantages, making it increasingly difficult for others to keep pace. Without proper governance, directors expose their organizations to costly failures. AI in the Boardroom equips leaders with the frameworks and tools to harness AI's potential responsibly, drive innovation, and avoid costly missteps. This book provides: An essential guide to the twelve foundational AI techniques with real-world commercial applications that board members need to understand. Insight into why most companies are not AI-ready due to immature data governance, along with actionable strategies to close this critical gap. A comprehensive AI governance framework that addresses AI-specific risks, which often fall outside the scope of traditional ERM frameworks. Real-world use cases to inspire how AI can drive innovation and competitive advantage, showcasing the art of the possible for your organization. This is a must-read for directors, C-suite executives, and governance leaders committed to unlocking AI's potential. Lead your organization with confidence, leveraging AI for growth while ensuring a solid governance foundation.

Contents:

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

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 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

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781637427873

1637427875

OCLC:

1496392545