Summary
Now, let's try to summarize what you have learned in this chapter. In this chapter, we focused on data-centric approaches for XAI. We learned the importance of explaining black-box models with respect to the underlying data, as data is the central part of any ML model. The concept of data-centric XAI might be new to many of you, but it is an important area of research for the entire AI community. Data-centric XAI can provide explainability to the black-box model in terms of data volume, data consistency, and data purity.
Data-centric explainability methods are still active research topics, and there is no single Python framework that exists that covers all of the various aspects of data-centric XAI. Please explore the supplementary Jupyter notebook tutorials provided at https://github.com/PacktPublishing/Applied-Machine-Learning-Explainability-Techniques/tree/main/Chapter03 to gain more practical knowledge on this topic.
We learned about the idea of thorough data...