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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

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

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

Social media and attention engineering

There is no better everyday example of how AI and machine learning are tied together. In a tweet in May 2022, Tesla CEO and now Twitter owner Elon Musk pointed toward these concerns with the Twitter feed algorithm.

Figure 2.2: Elon Musk’s take on Twitter’s algorithm

Figure 2.2: Elon Musk’s take on Twitter’s algorithm

Using social media data to manipulate voting trends and public opinion came into the spotlight with the infamous Cambridge Analytica scandal during the 2016 US presidential elections. Cambridge Analytica harvested Facebook data to change audience behavior for political and commercial causes. The use of bots, natural language, and visual image generation kept growing with the advent of technologies such as OpenAI’s GPT3 and Deepfakes. It is getting harder to distinguish between bots and real individuals as advanced technologies enable fake and AI-generated images and text. Deepfaking is a technique to generate human images that can...

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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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