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


The AIID is a collection of documented cases where AI systems have led to unexpected, negative outcomes. These incidents can range from minor inconveniences to significant disruptions or harm, and they highlight the need for continuous improvement in AI system design, implementation, and monitoring. By maintaining a record of these incidents, researchers, developers, and policymakers can learn from past mistakes, identify common patterns, and work toward developing more robust, safe, and responsible AI systems.

The AIID is an invaluable resource for understanding the potential risks and challenges associated with AI systems. It serves as a repository for incidents involving AI systems that have resulted in unintended consequences or negative outcomes. By studying these incidents, researchers and practitioners can gain insights into common pitfalls, vulnerabilities, and design flaws, ultimately contributing to the development of safer and more reliable AI technologies.