Summary

In this chapter, we provided you with an overview of explainability and interpretability toolkits, which are important tools for understanding how AI models make decisions, as well as the role of PETs in ensuring data privacy and security. This chapter reviewed several toolkits, such as Google Vertex Explainable AI, Amazon SageMaker Clarify, model interpretability in Azure Machine Learning, and IBM’s AIF360. These toolkits enable developers to implement disciplined approaches and tools for the transparency and explainability of AI-enabled decision-making, while PETs, such as differential privacy, homomorphic encryption, and federated learning, help protect sensitive data.

This chapter also highlighted the importance of synthetically generated data in AI development to mitigate bias, improve fairness, and assure regulatory compliance within legal and ethical constraints. Synthetic data generation can be used to create datasets that are balanced and representative of...