Practical explainable AI using Python: artificial intelligence model explanations using Python-based libraries, extensions, and frameworks

Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. What You'll Learn Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption Who This Book Is For AI engineers, data scientists, and software developers involved in driving AI projects/ AI products.

Includes index.

Chapter 1: Introduction to Model Explainability and Interpretability -- Chapter 2: AI Ethics, Biasness and Reliability -- Chapter 3: Model Explainability for Linear Models Using XAI Components -- Chapter 4: Model Explainability for Non-Linear Models using XAI Components -- Chapter 5: Model Explainability for Ensemble Models Using XAI Components -- Chapter 6: Model Explainability for Time Series Models using XAI Components -- Chapter 7: Model Explainability for Natural Language Processing using XAI Components -- Chapter 8: AI Model Fairness Using What-If Scenario -- Chapter 9: Model Explainability for Deep Neural Network Models -- Chapter 10: Counterfactual Explanations for XAI models -- Chapter 11: Contrastive Explanation for Machine Learning -- Chapter 12: Model-Agnostic Explanations By Identifying Prediction Invariance -- Chapter 13: Model Explainability for Rule based Expert System -- Chapter 14: Model Explainability for Computer Vision.