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)
1
Part 1: Bigot in the Machine – A Primer
4
Part 2: Enterprise Risk Observability Model Governance
9
Part 3: Explainable AI in Action

Part 2: Enterprise Risk Observability Model Governance

This section introduces the critical topics of explainability, risk observability, and model governance, and their relevance in the context of cloud computing platforms. The upcoming chapters cover several essential areas, such as model interpretability approaches, measuring and monitoring model drift, audit and compliance standards, and the Enterprise Starter Kit for Fairness, Accountability, and Transparency. The section also explores the concepts of bias removal, model robustness, and adversarial attacks. By reading through the chapters, you will gain a comprehensive understanding of these significant concepts and their impact on the development and deployment of AI models, enabling you to make informed decisions and ensure the ethical and trustworthy use of AI. These topics are discussed in detail across four chapters to provide you with a comprehensive understanding of these important concepts.

This section comprises the...