# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 2 Related Work  
Whilst explaining AI systems is not a new research topic, it is still in its early stages. Several survey articles have been published for the domain, including [17; 19; 25; 26; 27; 28]. Of these, ref. [19] is the most recent and complete, summarising all others into one. This survey of XAI includes details about the concepts, taxonomies, and research work up to December 2019 along with opportunities and challenges for new or future researchers in the field of XAI. [PERSON] et al. [19] divide the taxonomy of explainable scenarios into two main categories: (1) Transparent ML models that are self-explanatory or answers all or some of the previous questions (e.g., Linear/Logistic regression, Decision trees, K-NN, rule-based learning, and general additive models) and (2) post-hoc models, where a new method is proposed to explain the model for explanation of a decision of a shallow or deep model. The post-hoc category is further divided into model-agnostic, which can be applied to all models to extract specific information about the decision and model-specific, which are specific to the model in use e.g., for SVM, or DL models such as CNN. In contrasts to [PERSON]'s transparent model view, [PERSON] et al. [29] give credence to the black box concept by highlighting an alternative point of view about the explanation of AI systems and whether AI scientists can gain more through considering broader concepts. In this work, they focus on 'what-if questions', and highlight that decisions of a black box system must be justified and open to discussion and questioning. In [30], emphasis is put on bringing transparency and trust in AI systems by taking care of issues such as the 'Clever Hans' problem [31] and providing some level of explanation for decisions being made. The authors categorise explanations based on the content (e.g., explaining learned representations, individual predictions, model behaviour, and representative examples) and their methods (e.g., explaining with surrogates, local perturbations, propagation-based approaches, and meta-explanations). Explainability of DLs for structured data is limited. In the majority of cases, traditional ML techniques such as random forest, XGboost, SVM, logistic regression, etc. are used with explainability techniques LIME [32], SHAP [33], or more recently MANE [34] that is being used with CNN. These methods for explaining predictions from ML algorithms have become well established in the past few years. It is important to highlight that the majority of the XAI methods, which use DL networks such as CNN, show heatmaps [31] or saliency visualisations [35] for images input to the network. These techniques are also applied to other types of input data apart from images, including text [36] and time series data [10]. However, some of the techniques in these XAI are not general in the sense that they cannot be applied to different ML algorithms and/or input types or both. Hence, here we will discuss briefly the explainability of approaches used for DL models in three main categories of input data i.e., images, text, and time series data. We explain these application of explainability for various DL model inputs to frame our work--but we note the lack of application of such techniques for DL models using tabular (non-time series) data. **XAI in Images:** A well-explored area of research in XAI is proposing models (mainly using CNN [31; 37; 38]) that can interpret and classify an input image. When such models are explained, they benefit from the intuitive visual nature of the input. The portion of the image that influenced the model decision can be highlighted, which is relatively easily understood by different types of recipients e.g., end-user (customer) or data scientists. For example, researchers found Clever Hans [31] type issues in datasets, which are highly interpretable for this issue [31].