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


In this chapter, we provided an overview of the potential harms of AI and automated decision-making. The chapter reviewed examples of AI harm in hiring and recruitment, facial recognition, biased natural language models, discriminatory impact, attention engineering, social media, and AI’s environmental impact. It also discussed autonomous weapon systems and military use cases. It was important to look at these examples because they highlight the potential negative consequences of using AI and the need for proper governance and risk management. By understanding the potential risks of AI, we can work toward developing more responsible and ethical AI systems.

In the next chapter, the focus shifts toward the methods that make explainable and interpretable AI possible. It covers a taxonomy of machine learning interpretability approaches, including global and local methods, debugging, and audit. The advantages and disadvantages of these techniques will be reviewed, along...