The past years have been characterized by an upsurge in opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although DNNs have great generalization and prediction abilities, it is difficult to obtain detailed explanations for their behaviour. As opaque Machine Learning models are increasingly being employed to make important predictions in critical domains, there is a danger of creating and using decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing DNNs with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is intended to be the go-to handbook for anyone with a computer science background aiming to obtain an intuitive insight from Machine Learning models accompanied by explanations out-of-the-box. The article aims to rectify the lack of a practical XAI guide by applying XAI techniques in particular day-to-day models, datasets and use-cases. In each chapter, the reader will find a description of the proposed method as well as one or several examples of use with Python notebooks. These can be easily modified in order to be applied to specific applications. We also explain what the prerequisites are for using each technique, what the user will learn about them, and which tasks they are aimed at.

过去几年的特点是不透明自动决策支持系统的兴起，例如深度神经网络（DNN）。尽管 DNN 具有很强的泛化和预测能力，但很难获得其行为的详细解释。随着不透明的机器学习模型越来越多地被用来在关键领域做出重要预测，存在创建和使用不合理或不合法决策的危险。因此，人们普遍认为赋予 DNN 可解释性的重要性。可解释的人工智能 (XAI) 技术可以用来验证和认证模型输出，并通过可信性、责任性、透明度和公平性等理想概念来增强模型输出。本指南旨在成为具有计算机科学背景的任何人的首选手册，旨在从机器学习模型中获得直观的见解，并附带开箱即用的解释。本文旨在通过在特定的日常模型、数据集和用例中应用 XAI 技术来纠正实用 XAI 指南的缺乏。在每一章中，读者都会找到对所提出方法的描述以及一个或多个 Python 笔记本的使用示例。这些可以很容易地修改，以便应用于特定的应用程序。我们还解释了使用每种技术的先决条件、用户将了解这些技术的内容以及它们的目标任务。