# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 7 Related Work  
Over the last decade, a plethora of research has emerged that addresses PPM as an improvement and enhancement use case of process mining. Different techniques have been proposed and applied to predict different information about running process instances. These techniques are illustrated and compared in [30; 31]. In [30], the authors categorise approaches based on prediction type, input type, algorithms used, and tool availability. They categorised the studied approaches into numerical, categorical, and next activity prediction-based approaches. A contribution of the study in [31] is the consideration of the process-awareness perspective when categorising runtime PPM approaches.  
As an example of techniques that make use of the inherent transparency of the underlying predictive models, [32] uses Dynamic Bayesian Networks (DBN) to analyse the role of contextual attributes in predicting the next event of a running process instance. The former proposal benefits from the transparency of a DBN model, as the conditional probability distributions can be extracted at each state and time slice. A further example is proposed in [33], where a process-aware PPM approach is taken to predicting the remaining time of a running process instance while providing a transparent model. The latter paper provides an analysis of process performance using _flow analysis_ techniques. The authors of [34] propose the use of fuzzy neural networks to predict the possible outcomes of running process instances. Through this proposal in [34] the authors discuss the transparency of the predictive model, as it is possible to extract the relations between input features and generated predictions in terms of IF-THEN rules.  
As a complementary part of our present research, in [28] we study how characteristics of different XAI methods can be reflected in the generated explanations as well as how explainability methods can be compared according to different criteria. Furthermore, in [28], we propose an empirical analysis framework to study the impact of different PPM-related settings and ML model-related choices on the characteristics and expressiveness of the generated explanations. These explanations are generated on both global and local scales, i.e., for the whole event log and for individual process instances, respectively. We believe that the research introduced in this paper, along with the framework available in [28], constitutes a comprehensive framework. This framework enables study of the potential of explanations to reflect characteristics of contributing techniques and underlying phenomena taking place through the complete PPM workflow.  
The authors of [35] propose an approach that integrates Layer-wise Relevance Propagation (LRP) to explain the next activity predicted using an LSTM predictive model. This approach tends to propagate relevance scores backwards through the model in order to indicate which previous activities were crucial to obtaining the resulting prediction. Another approach to explaining LSTM decisions is presented in [36]. According to this approach, the total number of process instances in which a certain feature contributes to a prediction is identified at each timestamp for the whole event log. This identification is directed by SHAP values. In [36], the authors use the same approach to provide local explanations for running process instances.  
Explanations may be used to leverage predictive model performance, as proposed by [37]. Using LIME as a post hoc explanation technique to explain predictions generated using Random Forest, [37] identify feature sets which contributed to producing wrong predictions. After identifying these feature sets, their values were randomised, provided that they did not contribute to generating right predictions for other process instances. The resulting randomised event log was then used to retrain the model again until its perceived accuracy improved.