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

Facial recognition

One of the most appalling and alarming examples of facial recognition gone wrong is when African-Americans were classified as gorillas by Google’s facial recognition tool4 in 2015, which raised awareness and exposed the deep flaws in facial recognition technology.

The Gender Shades project is a pioneering research initiative led by computer scientist Joy Buolamwini, which exposes and addresses biases in facial recognition and analysis algorithms with respect to gender and skin tone. The study, published in 2018, scrutinized the performance of commercial facial recognition systems developed by prominent technology companies, such as IBM, Microsoft, and Face++. The groundbreaking discovery revealed that these AI systems exhibited higher error rates in classifying gender for darker-skinned and female faces compared to their lighter-skinned and male counterparts.

Researchers5 evaluated gender classification tools developed by IBM, Microsoft, and Face++ and...