A Learning Theoretic Perspective on Local Explainability

Understand

In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations.

• First, we tackle the traditional problem of performance generalization and bound the test-time accuracy of a model using a notion of how locally explainable it is.
• Second, we explore the novel problem of explanation generalization which is an important concern for a growing class of finite sample-based local approximation explanations.
• Finally, we validate our theoretical results empirically and show that they reflect what can be seen in practice.