Responsible AI in the Enterprise: Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI

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Key benefits

Learn ethical AI principles, frameworks, and governance

Understand the concepts of fairness assessment and bias mitigation

Introduce explainable AI and transparency in your machine learning models

Description

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.

What you will learn

Understand explainable AI fundamentals, underlying methods, and techniques

Explore model governance, including building explainable, auditable, and interpretable machine learning models

Use partial dependence plot, global feature summary, individual condition expectation, and feature interaction

Build explainable models with global and local feature summary, and influence functions in practice

Design and build explainable machine learning pipelines with transparency

Discover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Robert Hogg Nov 01, 2023

The content is well delivered and very relevant.

Feefo Verified review

Yiqiao Yin Nov 28, 2023

"Responsible AI in the Enterprise" serves as a crucial text for any professional grappling with the ethical implementation of artificial intelligence in today's data-driven environment. It delves deeply into the essence of creating machine learning systems that are not only efficient but also equitable and transparent. The book's comprehensive exploration of ethical AI principles, fairness frameworks, and governance is meticulously designed to impart a robust understanding of these critical areas. It offers a treasure trove of techniques and algorithms tailored to address the complexities of bias, fairness, and model governance, rendering it an indispensable guide for data scientists, AI practitioners, and IT professionals tasked with deploying AI responsibly.The narrative is adept at simplifying intricate concepts such as FairLearn and InterpretML, and tools like Google What-If Tool and IBM AI 360 Fairness tool. It serves as a practical manual, bringing to light the multifaceted nature of responsible AI, including the nuances of model interpretability, monitoring, and managing model drift. The author's focus on compliance recommendations ensures that readers are not only technically equipped but also ethically prepared to handle AI in enterprise settings. Particularly useful is the practical guidance on employing AI governance tools to uphold fairness, bias mitigation, explainability, and privacy compliance—concepts that are increasingly becoming non-negotiable in the realm of artificial intelligence.As the book culminates, it transitions from theory to application, enabling readers to convert their knowledge into action. It details the creation of explainable models using a variety of methods such as global and local feature summaries and counterfactual explanations, with an emphasis on the real-world applicability of such models. The inclusion of interpretability toolkits and fairness measures from leading cloud AI providers facilitates a deeper understanding of the current AI landscape. For those charged with the implementation of AI models within their organizations, including business stakeholders and AI ethicists, "Responsible AI in the Enterprise" is not just a learning resource—it's a roadmap to forging AI solutions that are as ethical as they are innovative.

Amazon Verified review

Advitya Gemawat Sep 23, 2023

I've found this book to be one of the most comprehensive and practical books on the topic of ethical, transparent, and compliant AI systems. The book covers the key concepts, tools, and techniques for creating fair, robust, and accountable machine learning models in an enterprise setting, and also served as a comprehensive guide to learn about real-life issues that arose due to bias in AI systems.The book starts with an intro to the ethical principles, frameworks, and governance standards for AI, and then dives into the various aspects of responsible AI, such as fairness, bias, explainability, privacy, and model drift. The book provides a thorough overview of the modern tools and algorithms for fairness assessment and bias mitigation, and also introduces usage of the toolkits offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, and shows how to use them in real-world scenarios.The book also includes code snippets and notebooks that demonstrate how to implement responsible AI practices using Python and using LLMs and foundation models as part of the latest Azure OpenAI service.On a side note, I also appreciate how the book was written in a gender-neutral language by using terms such as layperson instead of layman :)The book is suitable for anyone who wants to learn how to create ethical, transparent, and compliant AI systems, whether they are developers, data scientists, managers, or policymakers. I'd highly encourage folks interested in RAI and applying RAI in their organizations to go through this book.

Amazon Verified review

Chaitanya Yadav Sep 18, 2023

The book provides a comprehensive overview of the key concepts and practices of responsible AI, with a focus on the enterprise setting.The book initiates out by defining responsible AI and going into the moral and legal issues that companies need to think about while creating and implementing AI systemsOverall, I thought "Responsible AI in the Enterprise" was an excellent book. It is a must-read for anyone who is involved in the development or deployment of AI systems in the enterprise.

Amazon Verified review

tt0507 Sep 25, 2023

This book serves as a great introduction to Responsible AI due to its comprehensive and practical approach to ethical, transparent, and compliant AI systems. It covers key concepts, tools, and techniques for building responsible machine learning models in an enterprise context.The book starts with more conceptual ideas in the beginning such as ethical principles and governance standards for AI. Then dives into more model-specific topics such as fairness, bias, and model drifts. It was also great that the book includes code snippets from Python and Azure's OpenAI service.The book is highly recommended for those looking to apply Responsible AI in their organizations.

Amazon Verified review

Responsible AI in the Enterprise: Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI

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