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Responsible AI in the Enterprise

You're reading from   Responsible AI in the Enterprise Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781803230528
Length 318 pages
Edition 1st Edition
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Authors (2):
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Heather Dawe Heather Dawe
Author Profile Icon Heather Dawe
Heather Dawe
Adnan Masood Adnan Masood
Author Profile Icon Adnan Masood
Adnan Masood
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Bigot in the Machine – A Primer
2. Chapter 1: Explainable and Ethical AI Primer FREE CHAPTER 3. Chapter 2: Algorithms Gone Wild 4. Part 2: Enterprise Risk Observability Model Governance
5. Chapter 3: Opening the Algorithmic Black Box 6. Chapter 4: Robust ML – Monitoring and Management 7. Chapter 5: Model Governance, Audit, and Compliance 8. Chapter 6: Enterprise Starter Kit for Fairness, Accountability, and Transparency 9. Part 3: Explainable AI in Action
10. Chapter 7: Interpretability Toolkits and Fairness Measures – AWS, GCP, Azure, and AIF 360 11. Chapter 8: Fairness in AI Systems with Microsoft Fairlearn 12. Chapter 9: Fairness Assessment and Bias Mitigation with Fairlearn and the Responsible AI Toolbox 13. Chapter 10: Foundational Models and Azure OpenAI 14. Index 15. Other Books You May Enjoy

Policies and regulations

In this section, we will review the national policies and regulations pertaining to AI in various countries and regions. It is important to note the nuances, in similarities as well as differences, in these policies, since AI has a wide-ranging global impact.

United States

The United States (.U.S.) currently lacks a comprehensive regulation for AI at a national (federal) level. There have been a few different initiatives in the pipeline, including the Algorithmic Accountability Act, which aims to address the issues surrounding AI bias and discrimination. One notable effort is the National Institute of Standards and Technology (NIST) initiative called Towards a Standard for Identifying and Managing Bias in Artificial Intelligence. This initiative seeks to develop a framework to assess and mitigate biases in AI systems, focusing on transparency, explainability, and fairness. In the absence of an all-encompassing national standard to govern AI models, states...

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