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

Auditing checklists and measures

Along with compliance standards and code reviews, quantifying the results for model bias is a critical step in building accountable ML systems. In this section, we will provide a list of some of these checklists and measures.

Datasheets for datasets

Datasheets for datasets is an initiative aimed at improving transparency, accountability, and an understanding of the datasets used in the development and training of ML models. Introduced by Timnit Gebru, an AI ethicist and former co-leader of Google’s ethical AI team, as well as Kate Crawford and others, this initiative proposes using a standard way to report datasets, which its creators refer to as datasheets33. Their rationale was inspired by the electronics industry, where datasheets provide important information about the components being used:

“In the electronics industry, every component, no matter how simple or complex, is accompanied with a datasheet that describes its operating...