Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

Book Description

Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces.

Key Features

• Learn explainable AI tools and techniques to process trustworthy AI results
• Understand how to detect, handle, and avoid common issues with AI ethics and bias
• Integrate fair AI into popular apps and reporting tools to deliver business value using Python and associated tools

Book Description

Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex.

Hands-On Explainable AI (XAI) with Python will enable you to work with specific hands-on machine learning Python projects strategically arranged to enhance your grip on AI results analysis. The analysis includes building models, interpreting results with visualizations, and integrating understandable AI reporting tools and different applications.

You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source explainable AI tools for Python that can be used throughout the machine learning project life-cycle.

You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting machine learning model visualizations into user explainable interfaces.

By the end of this artificial intelligence book, you will possess an in-depth understanding of the core concepts of explainable AI.

What you will learn

• Plan for explainable AI through the different stages of the machine learning life-cycle
• Estimate the strengths and weaknesses of popular open-source explainable AI applications
• Examine how to detect and handle bias issues in machine learning data
• Review ethics considerations and tools to address common problems in machine learning data
• Share explainable AI design and visualization best practices
• Integrate explainable AI results using Python models
• Use explainable AI toolkits for Python in machine learning life-cycles to solve business problems

Who This Book Is For

This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book.

Some of the potential readers of this book include:

• Professionals who already use Python for as data science, machine learning, research, and analysis
• Data analysts and data scientists who want an introduction into explainable AI tools and techniques
• AI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications

中文：

书名：Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

解析AI应用程序中的黑盒模型，使其公平、值得信赖和安全。熟悉将可解释人工智能(XAI)部署到您的应用程序和报告界面的基本原则和工具。

Key Features

• 学习可解释的人工智能工具和技术，以处理可信的人工智能结果
• 了解如何发现、处理和避免人工智能道德和偏见方面的常见问题
• 将公平的人工智能集成到流行的应用程序和报告工具中，以使用Python和相关工具提供商业价值

Book Description

有效地将人工智能洞察转化为业务利益相关者需要仔细的规划、设计和可视化选择。描述问题、模型和变量之间的关系以及它们的发现通常是微妙的、令人惊讶的和复杂的技术。

可解释人工智能(XAI)和Python将使您能够处理特定的可动手操作的机器学习Python项目，这些项目经过战略性安排，以增强您对AI结果分析的把握。分析包括建立模型，用可视化解释结果，以及集成可理解的AI报告工具和不同的应用程序。

你将在Python、TensorFlow 2、谷歌云的XAI平台、谷歌协作和其他框架中构建XAI解决方案，以打开机器学习模型的黑匣子。本书将向您介绍几个可在整个机器学习项目生命周期中使用的开源、可解释的用于Python的AI工具。

您将学习如何探索机器学习模型结果、查看关键影响变量和变量关系、检测和处理偏差和道德问题，以及如何使用Python将预测与支持机器学习模型可视化集成到用户可解释的界面中。

在这本人工智能书的最后，你将对可解释AI的核心概念有一个深入的理解。

What you will learn

• 通过机器学习生命周期的不同阶段规划可解释的人工智能
• 评估流行的开源可解释人工智能应用的优势和劣势
• 研究如何检测和处理机器学习数据中的偏差问题
• 审查伦理考虑因素和工具，以解决机器学习数据中的常见问题
• 分享可解释的人工智能设计和可视化最佳实践
• Integrate explainable AI results using Python models
• 在机器学习生命周期中使用可解释的用于Python的AI工具包来解决业务问题

Who This Book Is For

这本书不是对Python编程或机器学习概念的介绍。你必须有一些基础知识和/或机器学习库的经验，比如SCRKIT–学习如何充分利用这本书。

Some of the potential readers of this book include:

• 已将Python用于数据科学、机器学习、研究和分析的专业人员
• Data analysts and data scientists who want an introduction into explainable AI tools and techniques
• 人工智能项目经理在其申请的接受阶段必须面对人工智能可解释性的合同和法律义务