Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to have better ends. Recent literature puts forward proposals of PAR systems that return valuable, practical recommendations. However, recommendations without sensible explanations prevent process owners from feeling engaged in the decision process or understanding why these interventions should be carried out. Therefore, the risk of process owners do not trust the PAR system and overlook these recommendations is high. This paper proposes a framework to accompany recommendations with sensible explanations based on the process behavior, the intrinsic characteristics, and the context in which the process is carried on. The paper illustrates the potential relevance of these explanations for process owners in two use cases.

Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.