  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.
