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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

Product type Paperback

Published in Jul 2023

Publisher Packt

ISBN-13 9781803230528

Length 318 pages

Edition 1st Edition

Tools

Azure

Concepts

Artificial Intelligence

Authors (2):

Heather Dawe

Adnan Masood

View More author details

Table of Contents (16) Chapters

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

Why subscribe?

15. Other Books You May Enjoy

The Responsible AI Toolbox

The Responsible AI Toolbox9 provides a range of tools and user interfaces to help developers and stakeholders of AI systems to better understand and monitor AI systems. The concept of responsible AI refers to a method of creating, evaluating, and using AI systems in a safe, ethical, and trustworthy way, making informed decisions, and taking responsible actions.

The toolbox includes four visualization widgets to analyze and make decisions about AI models:

The Responsible AI dashboard brings together various tools from the toolbox to provide a comprehensive view of responsible AI assessment and debugging. With this dashboard, you can identify model errors, understand why they happen, and take steps to address them. Additionally, the causal decision-making capabilities offer valuable insights to stakeholders and customers.
The Error Analysis dashboard helps identify model errors and identifies groups of data where a model performs poorly.

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Authors (2)

Adnan Masood

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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Dawe

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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