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Responsible AI in the Enterprise

You're reading from Responsible AI in the Enterprise

Product type Book

Published in Jul 2023

Publisher Packt

ISBN-13 9781803230528

Pages 318 pages

Edition 1st Edition

Languages

Concepts

Artificial Intelligence

Authors (2):

Adnan Masood

Heather Dawe

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Table of Contents (16) Chapters

Preface

1. Part 1: Bigot in the Machine – A Primer

2. Chapter 1: Explainable and Ethical AI Primer

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

Why subscribe?

15. Other Books You May Enjoy

Data, privacy, and security for Azure OpenAI Service

One of the most asked questions about Azure OpenAI (or any API service for that matter) is about data retention and customer controls.

Customer training data and fine-tuned OpenAI models are stored in Azure Storage, encrypted at rest with Microsoft-managed keys, and logically isolated with their Azure subscription and API credentials.

Customers can delete uploaded files and trained models via the DELETE API operation. Text prompts, queries, and responses may be temporarily stored for up to 30 days, encrypted, and are only accessible to authorized engineers for debugging, investigating misuse, or improving content filtering.

AI governance for the enterprise use of Azure OpenAI

Enterprise governance in Azure OpenAI involves ensuring that the service is used in a way that aligns with an organization’s overall goals and values, ensuring that the benefits of AI are maximized while minimizing potential harm. This includes...

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Authors (2)

Adnan Masood

Adnan Masood, PhD is an artificial intelligence and machine learning researcher, visiting scholar at Stanford AI Lab, software engineer, Microsoft MVP (Most Valuable Professional), and Microsoft's regional director for artificial intelligence. As chief architect of AI and machine learning at UST Global, he collaborates with Stanford AI Lab and MIT CSAIL, and leads a team of data scientists and engineers building artificial intelligence solutions to produce business value and insights that affect a range of businesses, products, and initiatives.

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Heather Dawe

Heather Dawe, MSc. is a renowned data and AI thought leader with over 25 years of experience in the field. Heather has innovated with data and AI throughout her career, highlights include developing the first data science team in the UK public sector and leading on the development of early machine learning and AI assurance processes for the National Health Service (NHS) in England. Heather currently works with large UK Enterprises, innovating with data and technology to improve services in the health, local government, retail, manufacturing, and finance sectors. A STEM Ambassador and multidisciplinary data science pioneer, Heather also enjoys mountain running, rock climbing, painting, and writing. She served as a jury member for the 2021 Banff Mountain Book Competition and guest edited the 2022 edition of The Himalayan Journal. Heather is the author of several books inspired by mountains and has written for national and international print publications including The Guardian and Alpinist.

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