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

Ethical AI upskilling and education

An AI governance framework helps establish the parameters to maintain public safety in the face of AI harm, promote fairness, and ensure efficient markets by preventing biased and anti-competitive practices. However, with AI bias being a socio-technical problem, human oversight remains important for detecting and building useful strategies to mitigate bias. For these reasons, it’s important not only to upskill workers in order to manage these technologies but also build ethical safeguards into the algorithms themselves.

Upskilling of knowledge workers is required to stay ahead of the AI regulatory curve and remain competitive in the market. With the rapid advancement of artificial intelligence and machine learning technology, it is becoming increasingly difficult for those without the proper skill set to keep up. Human oversight is important in artificial intelligence as it can help prevent errors and ensure that systems are working as...