40 Algorithms Every Programmer Should Know

Presenting strategies for explainability

For machine learning, there are fundamentally two strategies to provide explainability to algorithms:

• A global explainability strategy: This is to provide the details of the formulation of a model as a whole.

• A local explainability strategy: This is to provide the rationale for one or more individual predictions made by our trained model.

For global explainability, we have techniques such as Testing with Concept Activation Vectors (TCAV), which is used for providing explainability for image classification models. TCAV depends on calculating directional derivatives to quantify the degree of the relationship between a user-defined concept and the classification of pictures. For example, it will quantify ...