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 behavior. 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 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.

Credit risk assessments are vital to the operations of financial institutions. These activities depend on the availability of data. In many cases, the records of financial data processed by the credit risk models are frequently incomplete. Several methods have been proposed in the literature to address the problem of missing values. Yet, when assessing a company, there are some critical features that influence the final credit assessment. The availability of financial data also depends strongly on the country to which the company belongs. This is due to the fact there are countries where the regulatory frameworks allow companies to not publish their financial statements. In this paper, we propose a framework that can process historical credit assessments of a large number of companies, which were performed between 2008 and 2019, in order to treat the data as time series. We then used these time series data in order to fit two different models: a traditional statistics model (an autoregressive moving average model) and a machine-learning based model (a gradient boosting model). This approach allowed the generation of future credit assessments without the need for new financial data.

Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed explanations of their behaviour. As opaque machine learning models are increasingly being employed to make important predictions in critical environments, the danger is to create and use decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing machine learning models 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 meant to be the go-to handbook for any audience with a computer science background aiming at getting intuitive insights on machine learning models, accompanied with straight, fast, and intuitive explanations out of the box. This article aims to fill the lack of compelling XAI guide by applying XAI techniques in their particular day-to-day models, datasets and use-cases. Figure 1 acts as a flowchart/map for the reader and should help him to find the ideal method to use according to his type of data. In each chapter, the reader will find a description of the proposed method as well as an example of use on a Biomedical application and a Python notebook. It can be easily modified in order to be applied to specific applications.