# Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
## 2 Material and Methods
### Methods
#### 2.2.2 Explanation Techniques  
Explainable artificial intelligence (XAI), sometimes also called interpretable machine learning (IML), is a new research area. Several surveys about this topic have been published recently [15; 16; 17; 19] underlining its topicality. Explainability is presented either as inherent characteristic of an algorithm or as an approximation by other methods [37]. The latter is highly important for methods that have until recently been labeled as \"black-box\", such as artificial neural networks. To explain their predictions, however, numerous methods exist today [37; 38]. Generally, predictive modelling implies a trade-off: the reason for the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Type** & **Output Shape** & **Number of Parameters** \\ \hline conv2d & Conv2D & (None, 200, 150, 32) & 896 \\ conv2d\_1 & Conv2D & (None, 200, 150, 32) & 9248 \\ max\_pooling2d & Max\_Pooling2D & (None, 100, 75, 32) & 0 \\ dropout & Dropout & (None, 100, 75, 32) & 0 \\ conv2d\_2 & Conv2D & (None, 100, 75, 64) & 18,496 \\ conv2d\_3 & Conv2D & (None, 100, 75, 64) & 36,928 \\ max\_pooling2d\_1 & Max\_Pooling2D & (None, 50, 37, 64) & 0 \\ dropout\_1 & Dropout & (None, 50, 37, 64) & 0 \\ flatten & Flatten & (None, 118,400) & 0 \\ dense & Dense & (None, 128) & 15,155,328 \\ dropout\_2 & Dropout & (None, 128) & 0 \\ dense\_1 & Dense & (None, 7) & 903 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary and overall architecture of the CNN model used in this study. prediction versus how accurate it is. This means that the performance of complex models with non-linear combinations of inputs usually is better, but such models are harder or even impossible to understand. As pointed out before, deep learning models are typically on the extreme ends: they usually outperform all other machine learning techniques with regard to predictive accuracy (especially in image analysis tasks), but they are also the least interpretable. XAI/IML refer to approaches attempting to make machine learning models more explainable. The need for interpretability arises from an incompleteness in problem formalization [39], which means that for certain problems or tasks it is not enough to obtain the prediction (the what). The model must also explain how it came to the prediction (the why), because a correct prediction only partially solves the original problem. The following reasons drive the demand for interpretability and explanations [40]: compliance and trust related to uptake of health care applications, transparency and reproducibility of the AI decision-making process, and potentially mitigation of bias in health care. The challenge when using AI models as black boxes has resulted in a lack of accountability and trust in the decisions which XAI aims to rectify. Generally, XAI/IML methods can be categorized into  
* Intrinsic versus post hoc;
* Global versus local;
* Model-specific versus model-agnostic;
* Perturbation- or occlusion-based versus gradient-based. Intrinsic XAI/IML methods refer to techniques that are explainable by themselves (e.g., due to their simple structure, such as linear regression models), while post hoc methods explain the model's logic in retrospect after it was trained. Moreover, one distinguishes between local and global explanations. Although modular global explanations provide interpretation for the model as a whole, approaching it holistically, a local explanation provides interpretation for a specific observation (such as one particular image). Furthermore, an explanation technique can be model-specific if it depends on (parts of) its model, or model-agnostic, if it can be applied to any model. Occlusion- or perturbation-based methods manipulate parts of the image to generate explanations, while gradient-based methods compute the gradient of the prediction (or classification score) with respect to the input features. Another way to categorize XAI/IML methods is the manner in which they provide explanations.