Deep Learning with C++

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Explainability and Transparency in Deep Learning Models

In some applications, a prediction by itself is not enough. When a model influences medical care, lending, hiring, or safety decisions, people need to understand why it made that call and how much confidence to place in it. That is where explainability matters. This chapter looks at practical ways to make deep learning models more understandable, using methods you can deploy alongside your C++ inference stack.

We’ll frame explainability along three axes you’ll use in practice: local vs. global (explaining one decision versus model-wide behavior), post-hoc vs. intrinsic (analyzing a trained model versus designing for interpretability up front), and model-agnostic vs. model-specific. Model-agnostic ...