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Data Stewardship in Action

You're reading from   Data Stewardship in Action A roadmap to data value realization and measurable business outcomes

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Product type Paperback
Published in Feb 2024
Publisher
ISBN-13 9781837636594
Length 272 pages
Edition 1st Edition
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Author (1):
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Pui Shing Lee Pui Shing Lee
Author Profile Icon Pui Shing Lee
Pui Shing Lee
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Why Data Stewardship and Why Me? FREE CHAPTER
2. Chapter 1: From Business Strategy to Data Strategy to Data Stewardship 3. Chapter 2: How Data Stewardship Can Help Your Organization 4. Chapter 3: Getting Started with the Data Stewardship Program 5. Part 2:How to Become a Data Steward and Shine!
6. Chapter 4: Developing a Comprehensive Data Management Strategy 7. Chapter 5: People, Process, and Technology 8. Chapter 6: Establishing a Data Governance Organization 9. Chapter 7: Data Steward Roles and Responsibilities 10. Chapter 8: Effective Data Stewardship 11. Chapter 9: Supercharge Data Governance and Stewardship with GPT 12. Part 3:What Makes Data Stewardship a Sustainable Success?
13. Chapter 10: Data Stewardship Best Practices 14. Chapter 11: Theory versus Real Life 15. Chapter 12: Case Studies 16. Index 17. Other Books You May Enjoy

Embracing a responsible AI framework

A responsible AI framework is crucial for ensuring that AI systems are not only effective but also ethical, fair, and transparent. It is about making sure that as we harness the power of AI, we do so in a way that respects human values and societal norms. The Microsoft responsible AI framework (https://www.microsoft.com/en-us/ai/responsible-ai) is a set of guidelines and best practices for building and using AI systems in a way that respects human values and ethics. It is based on six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability, as shown in Figure 9.11:

Figure 9.11 – Responsible AI framework

Figure 9.11 – Responsible AI framework

The framework can help ensure the effective use of AI for data governance by considering the following:

  • Fairness: Ensuring that the AI systems do not create or reinforce unfair biases or outcomes for different groups of people or data subjects...
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