Название: Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
Автор: Pradeepta Mishra
Издательство: Apress
Год: 2022
Страниц: 356
Язык: английский
Формат: pdf (true)
Размер: 16.3 MB

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.

Explainable Artificial Intelligent (XAI) is a current need as more and more AI models are in production to generate business decisions. Thus, many users are also getting impacted by these decisions. One user may get favorably or unfavorably impacted. As a result, it’s important to know the key features leading to these decisions. It is often argued that AI models are quite black-box in nature because the AI model’s decisions cannot be explained, hence the adoptability of AI models is quite slow in the industry. The objective of this book is to explain the AI models in simple language using the above mentioned frameworks. Model interpretability and explainability are the key focuses of this book.

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.