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


As organizations increasingly turn to AI to drive critical business decisions, it is becoming increasingly important to understand how and why these systems are making the predictions they are making. This is known as AI explainability. Not only does AI explainability help to build trust in these systems but it also plays a crucial role in debugging and improving AI models. When we can understand how an AI algorithm works, we can have confidence in its results. However, if we cannot explain the workings of an AI system, we cannot be sure that it is making accurate predictions.

In enterprises and any other business setting in which explainability methods must be applied, there is a constant need to question whether the explainability challenges that come with more complex ML models can be justified, particularly when simpler ML models can do almost as good a job predictively.

In this chapter, we reviewed various methods for explaining AI models, including visualizing data...