Summary

In this chapter, we talked about the different elements of responsible AI, such as data privacy, security in machine learning systems, the different types of attacks and designing defense systems against them, transparency and accountability in the machine learning era, and how to use data and model governance to develop reliable and responsible models in practice.

This chapter and the two previous chapters, which make up Part 1 of this book, introduced important concepts in machine learning modeling and model debugging. Part 2 includes topics on how to improve machine learning models.

In the next chapter, you will learn about methods for detecting issues in machine learning models and opportunities for improving the performance and generalizability of such models. We will cover statistical, mathematical, and visualization techniques for model debugging with real-life examples to help you quickly start implementing these methods so that you can investigate and improve...