Explainable AI techniques

Different techniques are available to cater to various types of data, including tabular, image, and text data. Each data type presents its own set of challenges and complexities, requiring tailored methods to provide meaningful insights into the decision-making processes of ML models. This subsection will list various XAI techniques applicable to tabular, image, and text data. The following section will dive into the ones available as out-of-the-box features in Google Cloud.

Global versus local explainability

Explainability can be categorized into two categories: local and global explainability. These terms are sometimes referred to as local and global feature importance:

• Global explainability focuses on the overall impact of a feature on the model. This is usually obtained by calculating the average feature attribution values over the entire dataset. A feature with a high absolute value indicates that it significantly influenced the model’...