Interpretable AI: Building explainable machine learning systems

AI doesn’t have to be a black box. These practical techniques help shine a light on your model’s mysterious inner workings. Make your AI more transparent, and you’ll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements.

In Interpretable AI, you will learn:

• Why AI models are hard to interpret
• Interpreting white box models such as linear regression, decision trees, and generalized additive models
• Partial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning
• What fairness is and how to mitigate bias in AI systems
• Implement robust AI systems that are GDPR-compliant

Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You’ll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.