An Explainable Decision Support System for Predictive Process Analytics

TL;DR

This paper introduces an explainable predictive process analytics framework using Shapley Values, enhancing trust and understanding for business users by providing transparent predictions in process monitoring.

Contribution

It presents a novel framework integrating explainability into predictive process analytics with implementation in IBM Process Mining and real-world validation.

Findings

High prediction accuracy demonstrated on real-life data

Explanations were found to be intelligible to stakeholders

Framework successfully integrated into commercial process mining tools

Abstract

Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values. The framework has been implemented in the IBM Process Mining suite and commercialized for business users. The framework has been tested on real-life event data to assess the quality of the predictions and the corresponding evaluations. In particular, a user evaluation has been performed in order to understand if the explanations provided by the system were intelligible to process stakeholders.