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...