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

Part 3: Explainable AI in Action

This final section delves into the practical application of explainable AI and the challenges of deploying trustworthy and interpretable models in the enterprise. Real-world case studies and usage scenarios are presented to illustrate the need for safe, ethical, and explainable machine learning, and provide solutions to problems encountered in various domains. The chapters in this section explore code examples, toolkits, and solutions offered by cloud platforms such as AWS, GCP, and Azure, as well as Microsoft’s Fairlearn framework. Specific topics covered in this section include interpretability toolkits, fairness measures, fairness in AI systems, and bias mitigation strategies.

This section comprises the following chapters:

  • Chapter 7, Interpretability Toolkits and Fairness Measures – AWS, GCP, Azure, and AIF 360
  • Chapter 8, Fairness in AI Systems with Microsoft Fairlearn
  • Chapter 9, Fairness Assessment and Bias Mitigation...