Rule Extraction from Neural Networks is the process of distilling the knowledge embedded in a trained neural network into human-readable IF-THEN rules — converting opaque neural network decisions into transparent, verifiable logical rules that approximate the network's behavior.

Rule Extraction Approaches

• Decompositional: Extract rules from individual neurons/layers (e.g., analyzing hidden unit activation patterns).
• Pedagogical: Treat the network as a black box and learn rules from its input-output behavior.
• Eclectic: Combine both approaches — use internal network structure to guide rule learning.
• Decision Trees: Train a decision tree to mimic the neural network's predictions.

Why It Matters

• Transparency: Rules are inherently interpretable — engineers can read, verify, and challenge them.
• Validation: Extracted rules can be validated against domain knowledge to check if the network learned correct relationships.
• Deployment: In regulated environments, rules may be required instead of black-box neural networks.

Rule Extraction is translating neural networks into logic — converting opaque learned knowledge into transparent, verifiable decision rules.