# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 3 Research Problems  
The goal of our research is to study the effects of choices made through the PPM workflow on explanations as well as on how these explanations are able to reflect these choices. To this end, the research problem investigated here can be approached from two perspectives, the data side and the explanation side. It must be possible to understand the phenomena observed through explanations by understanding how the data transformation and preprocessing configurations change the original data. Furthermore, as illustrated in Section 2.2, interpretability and transparency implicitly accompany explainability and are inherent parts of it. On the way to achieving both, even partly, it should be possible for a stakeholder to trace a prediction back to its inputs. This should be done through a clear explanation that differs when the input data characteristics are different for the same ML predictive model and the same XAI method. Therefore, we have defined two Research Questions (RQ)s which capture this research problem on both sides.  
**RQ1: What are the effects of applying different preprocessing configurations on the event log data in terms of changed characteristics and relations between features?** We need to study the expected changes resulting from the use of different preprocessing configurations on features characteristics (e.g., cardinality levels) and relations both between features (e.g., correlations) and with the target feature. By understanding how the applied bucketing and/or encoding techniques transform the characteristics of an event log, it becomes possible to choose suitable ML models while taking into account the sensitivities of these models to different data characteristics. Finally, by observing the changes caused by a specific preprocessing choice, we may be able to understand several observed phenomena through a single explanation.  
**RQ2: How may explanations differ depending on the chosen preprocessing configurations?** After reaching an understanding of the changes occurring to the input data as a result of different preprocessing configurations, and considering the sensitivities of the used ML model(s), we need to understand how it is possible for an explanation to reflect different data characteristics. For example, what is the degree of similarity between the explanations generated by the same XAI method in explaining predictions of the same ML model with the same input data? In cases of dissimilarity, to what extent is this observation affected by the underlying data characteristics, e.g., collinearity between features? As another example, take linear models, which are transparent by nature [16], i.e., they are simulatable, decomposable, and algorithmically transparent. However, as confirmed by many studies [16; 19], even transparent models (including linear ones) lose their simulatability and decomposability when facing high dimensionality or heavily engineered features. Therefore, preprocessing configurations which increase the dimensionality of the feature set may influence the explainability of certain predictive models. Moreover, self-explanatory mechanisms provided by the predictive model itself have to be differentiated in terms of stability over several runs of the model. Here, by stability we mean that the same feature set has the same importance scores after querying the predictive model for the important features several times. Henceforth, we refer to this analysis as a _stability check_.