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


To summarize, the integration of Fairlearn and the Responsible AI Toolbox provides a comprehensive solution for responsible AI development and deployment, both within Azure as well as open source development. The dashboard brings together the power of several mature Responsible AI tools and libraries, providing a single pane of glass for conducting a holistic responsible assessment, debugging models, and making informed business decisions. With the Error Analysis dashboard, it is possible to identify model errors and discover cohorts of data for which the model underperforms.

The Fairness Assessment dashboard helps identify groups of people that may be disproportionately negatively impacted by an AI system. The Model Interpretability dashboard, powered by InterpretML, explains black-box models and helps users understand their global behavior and the reasons behind individual predictions.

Counterfactual Analysis and Causal Analysis provide actionable insights for data...