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)
1
Part 1: Bigot in the Machine – A Primer
4
Part 2: Enterprise Risk Observability Model Governance
9
Part 3: Explainable AI in Action

The role of internal AI boards in enterprise AI governance

A high-functioning and effective enterprise AI governance framework requires a clear understanding of the roles and responsibilities in question, effective communication between stakeholders, and regular reviews of and updates to automated decision-making systems. It is well established that AI model bias is a socio-technical problem, but it seems hard for organizations to grasp that ethical AI use cases vary based on the business use case. One size doesn’t fit all – and your AI bias will be nuanced and very specific to your industry, your use case, and your data.

Simply put, an internal AI board of governance is a group of people within an organization who are responsible for making decisions about the use of AI technologies within the enterprise. The board typically includes representatives from different departments within the organization, such as IT, marketing, sales, and operations. Its purpose is to...