# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 8 Conclusions  
In this research, a framework is implemented to study the effects of several choices made in the context of a PPM task on the relationships between data before and after preprocessing. Furthermore, we analyze explanations of predictions made for business process instances. These explanations are generated using either self-explanatory predictive models or post hoc methods. We study the ability of these methods to reflect underlying characteristics of the input data, and hence to improve the transparency of the prediction process, at least from the input perspective.  
Using two preprocessing configurations with two predictive models and two post hoc XAI methods may limit the generalisability of the observations and conclusions made in our experiments. However, we argue that our conclusions can provide useful insights about how certain preprocessing configurations lead to certain data characteristics that can expose sensitivities of certain predictive models, and hence become clear through the generated explanations. Furthermore, despite the limited number of techniques we experimented on, the same techniques have been reported in several studies to lead to the best performance results.  
Our study reveals inconsistencies between data characteristics, the way an ML model uses these data, and the way such usage is reflected in the resulting prediction explanations. Our study highlights situations in which data problems do not affect the accuracy of predictions, only the usefulness and consistency of explanations. Therefore, explainability should be seamlessly integrated into PPM workflow stages as an inherent task, not as a follow-up effort. We believe our findings can help stakeholders to make more informed decisions about which techniques to apply through the PPM workflow in light of the analysis provided here.  
[PERSON] drafted and revised the manuscript. [PERSON] was responsible for conceptualizing this work. [PERSON] was responsible for the formal analysis, methodology, and writing the original draft. [PERSON] was responsible for reviewing and editing this article. M.A.-E. and [PERSON] validated and supervised this work. [PERSON] was responsible for funding acquisition. All authors read and agree to the published version of the manuscript.  
This study was carried out through funding provided as part of the cognitive computing in socio-technical systems program granted to the last author, as the supervisor of the first author's PhD candidacy.  
Not applicable.  
Datasets used in the context of our experiments are available at 4 TU Centre for Research Data ([https://data.4 tu.nl/Eindhoven_University_of_Technology](https://data.4 tu.nl/Eindhoven_University_of_Technology), accessed on 10 August 2022), and the code for the experiments carried out in the context of this work is available at [https://anonymous.4 open.science/r/PPM_XAI_Comparison-1E8D/README.md](https://anonymous.4 open.science/r/PPM_XAI_Comparison-1E8D/README.md), accessed on 10 August 2022.  
The authors declare no conflict of interests.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ALE & Accumulated Local Effects \\ BPM & Business Process Management \\ GAM & General Additive Models \\ MutInfo & Mutual Information \\ ML & Machine Learning \\ PDP & Partial Dependence Plot \\ PFI & Permutation Feature Importance \\ PPM & Predictive Process Monitoring \\ RQ & Research Question \\ SHAP & SHapley Additive exPlanations \\ XAI & eXplainable Artificial Intelligence \\ XGBoost & eXtreme Gradient BOosting \\ \end{tabular}