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 business case for explainable AI

The objective of ML interpretability is to enable businesses to comprehend the rationale behind their AI models’ decisions. This is crucial because it can enhance decision-making processes, circumvent bias, and ensure AI models comply with regulations. Explainable AI also assists businesses in identifying and resolving AI model issues, ultimately improving system performance.

Explainable AI and responsible AI play a crucial role in businesses owing to their influence on return on investment (ROI), reputation, and morale. Implementing transparent and accountable AI systems can lead to more informed decision-making, enhanced trust from customers and stakeholders, and improved overall business performance. Conversely, neglecting to follow safe and ethical AI principles and compliance guidelines may have adverse consequences. Decreased trust from customers, employees, and partners could harm an organization’s reputation, while potential...