Interperetable AI

AI models can become so complex that even experts have difficulty understanding them—and forget  the nuances of a cluster of novel algorithms to a business stakeholder! Fortunately, there are techniques and best practices that will help make your AI systems transparent and interpretable. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Focused on practical methods that you can implement with Python, it teaches you to open up the black box of machine learning so that you can combat data leakage and bias, improve trust in your results, and ensure compliance with legal requirements. You''ll learn to identify when to utilize models that are inherently transparent, and how to mitigate opacity when you''re facing a problem that demands the predictive power of ahard-to-interpret deep learning model.

AI models can become so complex that even experts have difficulty understanding them—and forget about explaining the nuances of a cluster of novel algorithms to a business stakeholder! InterpretableAI is filled with cutting-edge techniques that will improve your understanding of how your AI models function.

InterpretableAI is a hands-on guide to interpretability techniques that open up the black box of AI. This practical guide simplifies cutting edge research into transparent and explainable AI, delivering practical methods you can easily implement with Python and opensource libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you toidentify the patterns your model has learned, and presents best practices for building fair and unbiased models.

How deep learning models produce their results is often a complete mystery, even to their creators. These AI"black boxes" can hide unknown issues—including data leakage, the replication of human bias, and difficulties complying with legal requirements such as the EU''s "right to explanation." State-of-the-art interpretability techniques have been developed to understand even the most complex deep learning models, allowing humans to follow an AI''s methods and to better detect when it has made a mistake.