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

Interpretability Toolkits and Fairness Measures – AWS, GCP, Azure, and AIF 360

Throughout this book, we have discussed the rationale for why responsible AI and AI governance are increasingly becoming critical disciplines for enterprises that wish to leverage AI. We also provided an overview of the methods that can be used to test that the machine learning models underpinning AI are safe, fair, and fit for purpose, along with introducing an AI assurance framework – AI STEPS FORWARD.

Leading cloud AI providers – the hyperscalers (namely AWS, Google, and Microsoft) – have recognized the need for AI explainability, and each has developed explainability toolkits designed to be used with its respective ML/AI development and MLOps environments. At the time of writing, the use of these explainability toolkits is not widespread across the industry. We believe that this is due to the following:

  • The relative immaturity of the widespread industry when it...