# XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework
## 1 Introduction  
### Problem Statement  
Predictive process monitoring (PPM) is a use case of process mining [1], which supports stakeholders by providing predictions about the future of a running business process instance [2, 3]. A process instance represents one specific execution instance out of all possible ones enabled by a business process model. Both process mining and PPM aim at informing stakeholders on how a business process is currently operating or expected to operate in near future. However, using ML models as prediction black-boxes does not help achieve this purpose. As stakeholders engagement is at the center of process mining tasks, performance as well as accuracy are no longer sufficient as metrics for evaluating a PPM prediction task. Stakeholders also need to assess the validity of the reasoning mechanisms being at the heart of the predictive model, as the model is predicting the future state of running business process instances. Justifying predictions to their recipients, in turn, increases the users' trust, engagement and advocacy of PPM mechanisms.  
The need to justify predictions prompted the introduction of eXplainable Artificial Intelligence (XAI) [4], with methods and mechanisms proposed to provide explanations of predictions generated by an ML model [5; 6; 7; 8; 9; 10; 11; 12]. These explanations are expected to reflect how a predictive model is influenced by different choices made through a given PPM workflow. Moreover, PPM tasks employ specific mechanisms that allow aligning process mining artefacts to ML models requirements. Different XAI methods exist to address explainability needs in the context of PPM, but the assumption remains that these methods should provide simple and consistent explanations for a given prediction scenario. However, explanations may vary due to changes in scenario settings or due to their underlying generation mechanisms. Studying how explanations of XAI methods may vary is therefore a crucial step to understand the settings suitable for employing these methods, as well as the way to interpret explanations in terms of the underlying influencing factors.  
With the increasing number of XAI methods which address different purposes and users needs through varying techniques, there is a need to compare different XAI methods outcomes given the same PPM workflow settings. Note that such a comparison is beneficial to identify combinations of techniques that if adopted through a PPM project can maximize the gains of applying XAI methods. Contrasting and differentiating XAI methods enables us to discover situations for which certain methods are better able to highlight data or ML models characteristics through generated explanations than others. Such differentiation is crucial to facilitate the selection of XAI methods, which not only suit the input characteristics, but are, in addition, well-fitting to the purpose of explaining PPM outcomes and to the target audience. Providing a guidance on XAI methods available choices based on experimental studies is a step towards integrating explainability as an indispensable stage of any project involving generation of predictions.