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

Google Vertex Explainable AI

Google Vertex Explainable AI is a toolkit that equips developers with cutting-edge algorithms and tools to gain valuable insights into their ML models. This toolkit offers several explainability algorithms, including decision trees and rule lists, providing a step-by-step explanation of how the model arrived at its prediction and highlighting the key rules used by the model in making its predictions. It offers feature-based and example-based explanations to provide a better understanding of the model’s decision-making process.

Google has worked hard to integrate its Explainable AI offering into its wider ML and AI development services. It offers a range of features, including algorithms and tools that help developers understand and debug their models. Its strength in data preparation and deep learning, including AutoML, makes it a good choice for businesses looking to develop and deploy ML models in a reliable and scalable manner. In our view...