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Debugging and Explaining AI Model Behavior

7 chapters · 34 lessons

1. 1. Mapping Model Behavior to Internal Representations

   4 lessons

   Learn to observe and measure what happens inside a model between input and output.

2. 2. Attribution Methods for Input Influence

   5 lessons

   Identify which parts of an input most strongly affect model outputs.

3. 3. Probing and Feature Detection

   5 lessons

   Test whether internal activations encode specific concepts, patterns, or behaviors.

4. 4. Causal Intervention and Circuit Analysis

   5 lessons

   Change internal model states to test whether identified features actually control behavior.

5. 5. Diagnosing Failure Modes in Production Systems

   5 lessons

   Apply interpretability to real debugging scenarios where models behave unexpectedly.

6. 6. Building Auditable Explanations for Governance

   5 lessons

   Create evidence-based explanations that support risk assessment and compliance requirements.

7. 7. Integrating Interpretability into Development Workflows

   5 lessons

   Embed interpretability practices into model evaluation, fine-tuning, and deployment pipelines.