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 reviewed the current standards landscape. You saw how different countries, professional bodies, and organizations implement best practices, governance, regulations, and policies around automated decision management systems. We provided an overview of national policies and regulations, attempts from professional bodies to establish industry standards, the contemporary landscape of technology toolkits, and auditing checklist metrics.

In many ways, the sheer number of different standards, regulatory frameworks, and guides for best practice is daunting. This is perhaps particularly true for those enterprise leaders who are non-technical regarding data science and ML, but who are seeking to lead their businesses to achieve the benefits that AI-driven service improvement can facilitate. This is one of the main reasons we wrote this book! We seek to demystify these assurance processes. At the core of all the frameworks and starter kits outlined previously is...