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

Summary

In this chapter, we saw that the Fairlearn toolkit is a comprehensive open source tool for assessing and improving the fairness of AI systems built by data scientists and developers. We established that it is essential for good MLOps practices to be able to validate the performance of models, explain how they work, and monitor their performance continuously in order to address these issues. As AI regulations and laws emerge, there is a need for deeper model transparency. The chapter provided an overview of the importance of fairness in AI systems. We started by discussing the concept of fairness and the various types of fairness-related harms that could occur in AI systems, then introduced the Fairlearn toolkit to help data scientists and AI practitioners promote fairness in their models. The Fairlearn toolkit includes a range of fairness metrics that could be used to assess the level of fairness in a model, and a variety of tools and techniques for mitigating fairness-related...