# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 2 Background
### eXplainable Artificial Intelligence  
Explainability, interpretability, and transparency are common terms with more or less the same meaning, referring to the problem of understanding and trusting the underlying mechanisms as well as the predictions of an ML model [14]. According to [15], 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\"_. In the context of our research, the decision maker is an ML-based predictive model. Moreover, in this context explanation has a broader meaning, including both the reasoning process behind the generation of predictions and the factors that contribute to reaching a given prediction. For example, it is crucial to know which data characteristics influence a prediction (e.g., in cases of \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Cen.Jd** & **Arpiment** & **id** & **Act.3** & **Act.2** & **Act.3** & **Act.4** & **Res.3** & **Res.2** & **Res.3** & **Monthly** & **Monthly** & **Monthly** & **Monthly** & **Regenerated** & **Cen.Jannual** \\ \hline A150 & C1620 & Cornt.app & valid.app & decide.app & clone.app & Jolu & Jolu & Jolu & Jolu & Jolu & Jolu & Jolu & Jolu & Jolu & Jolu \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature vector created for case with id = A150 from the log in Table 1 using _index-based_ encoding. imbalanced data or high correlations), as well as which features should be considered important to the predictive model. Interpretability is _the degree to which a human can consistently predict the model's result_[4]. This human prediction and the mimicking of the model's reasoning is based on the mental model, a human form of explanation with respect to how the model reached its decisions. In [4, 16], interpretability is considered equal to transparency. Transparency, however, can be regarded at three levels. The first level is _simulatability_, and refers to the human capability to simulate how a model reaches a prediction. The second level is _decomposability_, referring to the ability to understand a predictive model in terms of its elementary components, e.g., its inputs and hyperparameters. The last level, _algorithmic transparency_, denotes the understandability of the inner working of a prediction algorithm [4]. Model transparency itself can be an inherent characteristic or be achieved through explanation of a model. Transparent models are understandable on their own and satisfy one or all model transparency levels [16]. Linear models, decision trees, Bayesian models, rule-based learning, and General Additive Models (GAM) are all considered to be transparent and interpretable models. However, the mental models of humans and our ability to mimic the reasoning process of a predictive model, and hence to trust and accept its predictions, depend on several qualities that determine a good explanation. The composition, content, and quality of an explanation can be influenced, characterised, and evaluated according to its constructing approach. XAI methods, in turn, can be differentiated along several factors, e.g., the way an explanation is generated or the granularity and scope within which an explanation can be generalised. Other factors may include the time at which an XAI method is applied, the way an explanation is presented, and the user group to which an explanation is presented. Explanations can be generated in several ways, e.g., by providing _examples of process instances_ which are similar in the values of their attributes but different in their prediction outcome. Another way is to _visualise_ the intermediate representations and layers of a predictive model, with the aim of qualitatively determining what the has model learned [14]. _Explanation by feature relevance_ is one type of XAI method, and includes assigning importance scores to those features that contribute to the prediction process of an ML model. Corresponding XAI methods include, e.g., SHAP [5] and Partial Dependence Plots (PDP) [6].