# XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework
## 2 Preliminaries
### eXplainable Artificial Intelligence
For example, an ML engineer prefers gaining a holistic overview of the factors driving the reasoning process of a predictive model, whereas an end user is only interested in why a model made a certain prediction for a given instance. 3. **How to present. ** Choosing the form according to which an explanation is presented is determined by the way the explanation is generated, the characteristics of the end user (e.g., level of expertise), the scope of the explanation, and the purpose of generating an explanation (e.g., to visualise effects of feature interactions on decisions of the respective predictive model). Three categories of presentation forms were introduced in [15]. The first category comprises _visual explanations_ that use visual elements like saliency maps [10] and charts to describe deterministic factors of a decision in accordance with the respective perspective to be explained of a model. _Verbal explanation_ provides another way of presenting explanations where natural language is used to describe model reasoning (e.g., in recommender systems). The final form of presentation is _analytic explanation_ where a combination of numerical metrics and visualisations are used to reveal model structure or parameters, e.g., using heatmaps and hierarchical decision trees. 4. **When to explain. ** This dimension of an explainability approach is concerned with the point in time an explanation shall be provided. Agreeing on explainability being a subjective topic and depending on the receiver's understanding and needs, we may regard explainability provisioning from two perspectives. The first perspective considers explainability as gaining an understanding of decisions of a predictive model, being bounded by model characteristics. Adopting this perspective imposes explainability through mechanisms put in place while constructing the model to obtain a white-box predictive model, i.e., _intrinsic explanation_. On the other hand, using an explanation method to understand the reasoning process of a model in terms of its outcomes is called _post-hoc explanation_. The latter provides an understanding in terms of the whole reasons behind the mapping process between inputs and outputs. Moreover, it provides a holistic view of input characteristics which led to predictive model decisions. 5. **Explain to Whom. ** Studying the target group of each explainability solution becomes necessary to tailor the explanations and to present them in a way that maximizes the interpretability of a predictive model, forming a mental model of it. The receivers of an explanation should be at the center of attention when designing an explainability solution. These receivers can be further categorised into different user groups including novice users, decision makers and experienced users, system practitioners, and regulatory bodies [14; 15]. Targeting each user group with suitable explanations contributes to achieve the explanation process purpose. The purpose of an explanation may be to understand how a predictive model works or how it makes decisions, or which patterns are formed in the learning process. Therefore, it is crucial to understand each user group, identify its relevant needs, and define design goals accordingly. The various dimensions of explainability are tightly interrelated, i.e., a particular choice in one dimension might affect the choices made in other dimensions. Making choices on the different dimensions of the presented taxonomy is guided by several factors, which include the following: (1) enhancing understandability and simplicity of the explainability solution; (2) the availability of software implementations of explainability methods and whether these implementations are model-agnostic or specific; (3) the type of output of each explanation method and the subsequent choice of a suitable presentation type which suits the target group. When facing explanations that serve different user groups with different explainability goals, putting explainability evaluation techniques in place becomes a necessity.