# XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework
## 2 Preliminaries
### eXplainable Artificial Intelligence  
PPM inherits the challenges faced by ML approaches, as a reasonable consequence of employing the latter approaches. One of these challenges concerns the need to gain user trust in the generated predictions,which promotes growing interest in the field of explainable artificial intelligence (XAI). An explanation is _\"a human-interpretable description of the process by which a decision maker took a particular set of inputs and reached a particular conclusion\"_[13]. In the context of our research, the decision maker is an ML-based predictive model. Transparency is considered a crucial aspect of explainability. Model transparency can be an inherent characteristic or be achieved through an explanation. Transparent models are understandable on their own and satisfy one or all model transparency levels [14]. Linear models, decision trees, Bayesian models, rule-based learning, and General Additive Models (GAM) [4] may all be considered as transparent (i.e., interpretable) models. Note that the degree of transparency realised through an explanation may be affected by several factors, e.g., the incompleteness of the problem formulation or understanding, the level of complexity of an explainability solution, and the number as well as length of cognitive chunks made available to the user through an explanation. Several approaches are proposed under the umbrella of explainability. Explanations construction approaches can be categorised along several dimensions (cf. Figure 1). We use dashed rectangles to highlight those techniques that we further consider for studying in our comparative framework. In the following, we illustrate these explainability dimensions. Figure 1: Explainability taxonomy in ML. **How to explain. ** This dimension is concerned with the approach used to explain how a predictive model derives its predictions based on the given inputs. Corresponding approaches have been categorised along different perspectives including design goals and evaluation measures, transparency of the explained model and explanation scope [14; 15], granularity [16], and relation to the black-box model [17]. For example, a group of approaches tend to generate an _explanation by simplification_. These approaches simplify a complex model by using a more interpretable model called a _surrogate_ or _proxy model_. The simplified model is supposed to generate understandable predictions that achieve an accuracy level comparable to the black-box one. Another group of approaches study _feature relevance_. They aim to trace back the importance of a feature for deriving a prediction. Another family of approaches tend to _explain by example_. Approaches from this category tend to select representative samples that allow for insights into the model's internal reasoning [14; 16]. The final category in this dimension explains through _visualisation_, i.e., intermediate representations and layers of a predictive model are visualised with the aim to qualitatively determine what a model has learned [16]. Approaches belonging to this XAI dimension are further categorised into _model-agnostic_ or _model-specific_ methods. Model-agnostic approaches are able to explain any type of ML predictive models, whereas model-specific approaches can only be used on top of specific models. 2. **How much to explain. ** An explanation may be generated at various levels of granularity. An explanation effort can be localised to a specific instance, i.e., _local explanation_, and it can provide global insights into which factors contributed to the decision of a predictive model, i.e., to generate _global explanations_. The scope of an explanation and, subsequently, the chosen technique depend on several factors. One of these factors is the purpose of the explanation, e.g., whether it shall allow debugging the model or gaining trust into its predictions. Target stakeholders constitute another deterministic factor.