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

Getting started with enterprise AI governance

In some business settings such as finance and healthcare, the machine learning algorithms that underpin AI are already regulated. For example, in the UK, the Financial Conduct Authority (FCA) is responsible for ensuring that credit agencies who use risk modeling (typically logistic regression) to estimate the likelihood of a customer defaulting on a loan do so in ways that are consistently fair to the customer. The credit agency must assure the FCA that the machine learning models they use for this are not biased for certain customer groups, are robust estimators of default risk, and similar assurances.

So, we can say that in some instances, AI Assurance Frameworks already exist and are used regularly in business today. However, as the use of AI grows exponentially across industry and the machine learning models used to produce this AI increase in their power and complexity, there is arguably a growing requirement for AI Assurance in...