Trustworthy AI

Trustworthy AI

by Beena Ammanath
Released March 2022
Publisher(s): Wiley
ISBN: 9781119867920

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

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.  

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Foreword
  5. Preface
  6. Acknowledgments
  7. Introduction
  8. Chapter 1: A Primer on Modern AI
    1. The Road to Machine Intelligence
    2. Basic Terminology in AI
    3. Types of AI Models and Use Cases
    4. New Challenges for the Modern AI Era
    5. Notes
  9. Chapter 2: Fair and Impartial
    1. A Longstanding Ethical Question
    2. The Nature of Bias in AI
    3. Tradeoffs in Fairness
    4. Leading Practices in Promoting Fairness
    5. Toward a Fairer Future in AI
    6. Notes
  10. Chapter 3: Robust and Reliable
    1. Robust vs Brittle AI
    2. Developing Reliable AI
    3. The Challenge of Generalizable Deep Learning
    4. Factors Influencing AI Reliability
    5. Robustness and Bad Actors
    6. Consequences Worth Contemplating
    7. Leading Practices in Building Robust and Reliable AI
    8. Driving Toward Robust and Reliable Tools
    9. Notes
  11. Chapter 4: Transparent
    1. Defining the Nature of Transparency in AI
    2. The Limits of Transparency
    3. Weighing the Impact on the Stakeholders
    4. Taking Steps into Transparency
    5. Trust from Transparency
    6. Notes
  12. Chapter 5: Explainable
    1. The Components of Understanding AI Function
    2. The Value in Explainable AI
    3. Factors in Explainability
    4. Technical Approaches to Fostering Explainability
    5. Leading Practices in Process
    6. The Explainable Imperative
    7. Notes
  13. Chapter 6: Secure
    1. What Does AI Compromise Look Like?
    2. How Unsecure AI Can Be Exploited
    3. The Consequences from Compromised AI
    4. Leading Practices for Shoring‐Up AI Security
    5. Securing the Future with AI
    6. Notes
  14. Chapter 7: Safe
    1. Understanding Safety and Harm in AI
    2. Aligning Human Values and AI Objectives
    3. Technical Safety Leading Practices
    4. Seeking a Safer Future with AI
    5. Notes
  15. Chapter 8: Privacy
    1. Consent, Control, Access, and Privacy
    2. The Friction Between AI Power and Privacy
    3. Beyond Anonymization or Pseudonymization
    4. Privacy Laws and Regulations
    5. Leading Practices in Data and AI Privacy
    6. The Nexus of AI Trust and Privacy
    7. Notes
  16. Chapter 9: Accountable
    1. Accountable for What and to Whom?
    2. Balancing Innovation and Accountability
    3. Laws, Lawsuits, and Liability
    4. Leading Practices in Accountable AI
    5. Accounting for Trust in AI
    6. Notes
  17. Chapter 10: Responsible
    1. Corporate Responsibility in the AI Era
    2. Motivating Responsible AI Use
    3. Balancing Good, Better, and Best
    4. Leading Practices in the Responsible Use of AI
    5. Trust Emerging from Responsibility
    6. Notes
  18. Chapter 11: Trustworthy AI in Practice
    1. Step 1 – Identify the Relevant Dimensions of Trust
    2. Step 2 – Cultivating Trust Through People, Processes, and Technologies
    3. Guidelines for Action on Trustworthy AI
    4. Taking the Next Steps
    5. Note
  19. Chapter 12: Looking Forward
    1. Note
  20. Index
  21. End User License Agreement

Product information

  • Title: Trustworthy AI
  • Author(s): Beena Ammanath
  • Release date: March 2022
  • Publisher(s): Wiley
  • ISBN: 9781119867920