# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 1 Introduction  
Explainable Artificial Intelligence (XAI) is about opening the \"black box\" decision making of Machine Learning (ML) algorithms so that decisions are transparent and understandable. This ability to explain decision models is important to data scientists, end-users, company personnel, regulatory authorities, or indeed any stakeholder who has a valid merit to ask questions about the decision making of such systems. As a research area, XAI incorporates a suite of ML techniques that enables human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners [1]. Interest in XAI research has been growing along with the capabilities and applications of modern AI systems. As AI makes its way to our daily lives, it becomes increasingly crucial for us to know how underlying opaque AI algorithms work. XAI has the potential to make AI models more trustworthy, compliant, performant, robust, and easier to develop. That can in turn widen the adoption of AI solutions and deliver greater business value. A key development in the complexity of AI systems was the introduction of AlexNet deep model [2], a Convolutional Neural Network (CNN) that utilises two Graphical Processing Units (GPUs) for the first time, enabling the training of a model on a very large training dataset whilst achieving state-of-the-art results. With 10 hidden layers in the network, AlexNet was a major leap in Deep Learning (DL), a branch of ML that produces complex multi-layer models that present particular challenges for explainability. Since AlexNet's unveiling in 2012, other factors have boosted the rapid development of DL:Availability of big data, cloud computing growth, powerful embedded chips, reduction in the cost of systems with high computational power and memory, and the achievement of a higher performance of DL models over traditional approaches. In some application areas, these models achieve as good as human level performance, such as in object recognition [3,4], object detection [5,6], object tracking [7]) or games (e.g., beating AlphaGo champion [8]), stock price predictions [9], time series forecasting [10,11], and health [12]. In an early work in 2010, researchers in [13] focused on explaining individual decisions taken by classifiers. From 2012, there has been a year on year improvement in the accuracy of deep learning models, accompanied by greater complexity. Researchers are actively investigating the real-life implications associated with the deployment of these types of models. In addition to ethical concerns, such as privacy or robot autonomy, there are other issues at the heart of ML that are critical to handle. For example, potentially biased decisions due to bias in the training data and the model; a system wrongly predicting/classifying an object with high confidence; lack of understanding of how a decision is taken or what input features were important in this decision; and downstream legal complications, such as the lack of adherence to the \"right to explanation\" under EU General Data Protection Regulation (GDPR) rule [14]. For example, a customer whose loan application has been rejected has the right to know why their application was rejected. Some models are used to make decisions that have life threatening implications, such as the interpretation of potential cancer scans in healthcare. Currently, a doctor is needed as an intermediate user of the system to take the final decision. Other AI scenarios aim to remove the intermediate user. For example, the use of fully autonomous cars would cede full control to the associated AI-based driving system. DL models are at the heart of these types of complex systems. Examples such as these emphasise the critical nature of explaining, understanding, and therefore controlling the decisions of DL models. Explainability means different things, depending upon the user (audience/stakeholder) of the explanation and the particular concerns they wish to address via an explanation.