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

AI-powered inequity and discrimination

In a jaw-dropping revelation that left many questioning the fairness of cutting-edge technology, the credit limit algorithm13 used by the prestigious Apple Card faced shocking accusations of gender bias. The supposedly sophisticated AI-driven system, designed to streamline the credit experience for millions of users, stood exposed when it was discovered that women received significantly lower credit limits compared to their male counterparts with strikingly similar financial backgrounds. This mind-boggling disparity sparked an outcry and provoked fierce debate, leaving the public to wonder whether even the giants of technology could succumb to the antiquated prejudices plaguing society.

A controversial study from Stanford University by Wang and Kosinski, Deep neural networks are more accurate than humans at detecting sexual orientation from facial images,14 which was dubbed as an artificially intelligent radar, claimed that widely used facial...