Explainable Predictive Process Monitoring: A User Evaluation
This work addresses the need for transparency in predictive process monitoring to foster trust among users, though it is incremental as it evaluates existing explanation approaches rather than introducing new ones.
The paper tackled the problem of explaining black-box predictive process monitoring models to users by conducting a user evaluation to assess the understandability and usefulness of explanation plots for decision-making. The results showed that explanation plots were generally understandable and useful for business process management users, but differences existed in comprehension and usage based on users' machine learning expertise.
Explainability is motivated by the lack of transparency of black-box Machine Learning approaches, which do not foster trust and acceptance of Machine Learning algorithms. This also happens in the Predictive Process Monitoring field, where predictions, obtained by applying Machine Learning techniques, need to be explained to users, so as to gain their trust and acceptance. In this work, we carry on a user evaluation on explanation approaches for Predictive Process Monitoring aiming at investigating whether and how the explanations provided (i) are understandable; (ii) are useful in decision making tasks;(iii) can be further improved for process analysts, with different Machine Learning expertise levels. The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for Business Process Management users -- with and without experience in Machine Learning -- differences exist in the comprehension and usage of different plots, as well as in the way users with different Machine Learning expertise understand and use them.