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Responsible AI in the Enterprise

By Adnan Masood , Heather Dawe
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  1. Free Chapter
    Chapter 1: Explainable and Ethical AI Primer
About this book
Responsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations. By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.
Publication date:
July 2023
Publisher
Packt
Pages
318
ISBN
9781803230528

 

Part 1: Bigot in the Machine – A Primer

This section introduces the importance of Explainable AI (XAI) and its challenges. It is a primer on XAI and ethical AI for model risk management, defining key concepts and terms. The section also presents several stories that highlight the dangers of unexplainable and biased AI, emphasizing the need for different approaches to address similar problems. Overall, this section serves to show you why you should ensure that the AI developed and used within your enterprise is explainable and effectively governed so it is auditable, providing you with a deeper understanding of XAI and how to integrate it into your AI model development and deployment strategies.

This section comprises the following chapters:

About the Authors
  • 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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