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

Policies and regulations

In this section, we will review the national policies and regulations pertaining to AI in various countries and regions. It is important to note the nuances, in similarities as well as differences, in these policies, since AI has a wide-ranging global impact.

United States

The United States (.U.S.) currently lacks a comprehensive regulation for AI at a national (federal) level. There have been a few different initiatives in the pipeline, including the Algorithmic Accountability Act, which aims to address the issues surrounding AI bias and discrimination. One notable effort is the National Institute of Standards and Technology (NIST) initiative called Towards a Standard for Identifying and Managing Bias in Artificial Intelligence. This initiative seeks to develop a framework to assess and mitigate biases in AI systems, focusing on transparency, explainability, and fairness. In the absence of an all-encompassing national standard to govern AI models, states...