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

Technology toolkits

Along with guidance documents and PowerPoint, enterprises need toolkits that can actually parse the datasets, models, and code to identify the underlying biases and provide practical ways to address these concerns. The following subsections explain some such tools and libraries that offer these capabilities.

Microsoft Fairlearn

Microsoft Fairlearn24 is an open source Python library to assess and improve the fairness of ML models, and it has a wide range of algorithms to compare and mitigate bias in predictive models, as well as visualization tools to explore and analyze model performance. Fairlearn is designed to help data scientists and developers build more equitable and inclusive ML models by providing them with the tools to measure and address unfairness in their models. The library is part of Microsoft’s RAI efforts and is freely available for use by anyone.

Figure 5.4: The Fairlearn toolkit

Figure 5.4: The Fairlearn toolkit

In Chapters 8 and 9, we...