The Building Blocks of Interpretability

1. 1Identify individual interpretability techniques to combine
2. 2Analyze the interfaces that arise from combining techniques
3. 3Explore the combinatorial space of combined techniques to discover new insights

Data scientists and machine learning engineers on a team benefit from understanding how to combine interpretability techniques to improve model transparency and explainability

💡 Combining interpretability techniques can create more powerful and informative interfaces than using individual techniques in isolation

🔍 Combining interpretability techniques unlocks new insights into model behavior