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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

Foundational Models and Azure OpenAI

“AI is one of the most transformative technologies of our time and has the potential to help solve many of our world’s most pressing challenges. By bringing together OpenAI’s breakthrough technology with new Azure AI supercomputing technologies, our ambition is to democratize AI – while always keeping AI safety front and center – so everyone can benefit.”

– Satya Nadella, CEO, Microsoft

“We also need enough time for our institutions to figure out what to do. Regulation will be critical and will take time to figure out; although current-generation AI tools aren’t very scary, I think we are not that far away from potentially scary ones.”

– Sam Altman, CEO, OpenAI

In today’s rapidly advancing technological landscape, organizations look for ways to leverage artificial intelligence (AI) to drive innovation and solve complex problems. Organizations typically...

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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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