What is Model Interpretability in Deep Learning and why is it important?

Model interpretability refers to understanding and explaining how deep learning models make predictions, which is critical for trust and accountability.

Deep learning models are often considered black boxes because their internal representations are complex and non-linear.

Interpretability aims to make these decisions understandable.

Why it matters:

Types: