Book Image

Responsible AI in the Enterprise

By : Adnan Masood, Heather Dawe
5 (1)
Book Image

Responsible AI in the Enterprise

5 (1)
By: Adnan Masood, Heather Dawe

Overview of 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.
Table of Contents (16 chapters)
Part 1: Bigot in the Machine – A Primer
Part 2: Enterprise Risk Observability Model Governance
Part 3: Explainable AI in Action

To get the most out of this book

To get the most out of this book, it is important to understand the context and target audience. This book is focused on responsible AI and machine learning model governance, providing in-depth coverage of key concepts such as explainable and ethical AI, bias in AI systems, model interpretability, model governance and compliance, fairness and accountability in AI, data governance, upskilling, and education for ethical AI. The target audience includes data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for building and deploying AI models in their organizations.

To maximize the benefits of this book, you should have a basic understanding of machine learning and AI. It is recommended to read the chapters in order to build a comprehensive understanding of the topics covered. Additionally, the hands-on examples and practical guidance provided in the book can be applied to real-world situations and can be used as a reference for future projects.

We sincerely hope you enjoy reading this book as much as we enjoyed writing it.

Software/hardware covered in the book

Operating system requirements

Jupyter Notebook (Python 3.x)

Windows, macOS, or Linux

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

This book is filled with references to the classic science fiction novel, The Hitchhiker’s Guide to the Galaxy, one of my favorite books of all time. So, excuse the puns and whimsical language as I pay homage to the humor and creativity of Douglas Adams. May this book guide you on your own journey through the world of AI and machine learning, just as the Guide guided Arthur Dent on his interstellar adventures.