DL Book 10 - Ethics and Bias in Artificial Intelligence for Dummies

Table of Contents

1. Introduction

1.1 What is Artificial Intelligence?

1.2 Why is Ethics and Bias Important in AI?

1.3 How to Use This Book

2. Ethical Principles and Frameworks for AI

2.1 What are Ethical Principles and Frameworks?

2.2 Examples of Ethical Principles and Frameworks for AI

2.3 How to Apply Ethical Principles and Frameworks to AI Projects

3. Sources and Types of Bias in AI

3.1 What is Bias and How Does it Affect AI?

3.2 Sources of Bias in AI Data, Algorithms, and Systems

3.3 Types of Bias in AI Outputs and Outcomes

4. Methods and Tools for Detecting and Mitigating Bias in AI

4.1 What are the Challenges and Benefits of Addressing Bias in AI?

4.2 Methods and Tools for Detecting Bias in AI Data, Algorithms, and Systems

4.3 Methods and Tools for Mitigating Bias in AI Outputs and Outcomes

5. Case Studies and Best Practices for Ethical and Fair AI

5.1 What are the Key Elements of Ethical and Fair AI?

5.2 Case Studies of Ethical and Fair AI in Different Domains and Applications

5.3 Best Practices and Recommendations for Ethical and Fair AI Development and Deployment 6. Future Trends and Challenges for Ethics and Bias in AI

6.1 What are the Emerging and Future Technologies and Issues in AI?

6.2 What are the Potential Risks and Opportunities for Ethics and Bias in AI? 6.3 How to Prepare for the Future of Ethics and Bias in AI?