Trustworthy AI: A Business Guide for Navigating Trust and Ethics in AI
- Length: 224 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2022-03-22
- ISBN-10: 1119867924
- ISBN-13: 9781119867920
- Sales Rank: #88486 (See Top 100 Books)
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.
Cover Table of Contents Title Page Copyright Foreword Preface Acknowledgments Introduction Chapter 1: A Primer on Modern AI The Road to Machine Intelligence Basic Terminology in AI Types of AI Models and Use Cases New Challenges for the Modern AI Era Notes Chapter 2: Fair and Impartial A Longstanding Ethical Question The Nature of Bias in AI Tradeoffs in Fairness Leading Practices in Promoting Fairness Toward a Fairer Future in AI Notes Chapter 3: Robust and Reliable Robust vs Brittle AI Developing Reliable AI The Challenge of Generalizable Deep Learning Factors Influencing AI Reliability Robustness and Bad Actors Consequences Worth Contemplating Leading Practices in Building Robust and Reliable AI Driving Toward Robust and Reliable Tools Notes Chapter 4: Transparent Defining the Nature of Transparency in AI The Limits of Transparency Weighing the Impact on the Stakeholders Taking Steps into Transparency Trust from Transparency Notes Chapter 5: Explainable The Components of Understanding AI Function The Value in Explainable AI Factors in Explainability Technical Approaches to Fostering Explainability Leading Practices in Process The Explainable Imperative Notes Chapter 6: Secure What Does AI Compromise Look Like? How Unsecure AI Can Be Exploited The Consequences from Compromised AI Leading Practices for Shoring‐Up AI Security Securing the Future with AI Notes Chapter 7: Safe Understanding Safety and Harm in AI Aligning Human Values and AI Objectives Technical Safety Leading Practices Seeking a Safer Future with AI Notes Chapter 8: Privacy Consent, Control, Access, and Privacy The Friction Between AI Power and Privacy Beyond Anonymization or Pseudonymization Privacy Laws and Regulations Leading Practices in Data and AI Privacy The Nexus of AI Trust and Privacy Notes Chapter 9: Accountable Accountable for What and to Whom? Balancing Innovation and Accountability Laws, Lawsuits, and Liability Leading Practices in Accountable AI Accounting for Trust in AI Notes Chapter 10: Responsible Corporate Responsibility in the AI Era Motivating Responsible AI Use Balancing Good, Better, and Best Leading Practices in the Responsible Use of AI Trust Emerging from Responsibility Notes Chapter 11: Trustworthy AI in Practice Step 1 – Identify the Relevant Dimensions of Trust Step 2 – Cultivating Trust Through People, Processes, and Technologies Guidelines for Action on Trustworthy AI Taking the Next Steps Note Chapter 12: Looking Forward Note Index End User License Agreement
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