Explainability in Process Outcome Prediction: Guidelines to Obtain
Interpretable and Faithful Models
- URL: http://arxiv.org/abs/2203.16073v5
- Date: Sun, 30 Jul 2023 14:31:42 GMT
- Title: Explainability in Process Outcome Prediction: Guidelines to Obtain
Interpretable and Faithful Models
- Authors: Alexander Stevens, Johannes De Smedt
- Abstract summary: We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction.
This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
- Score: 77.34726150561087
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although a recent shift has been made in the field of predictive process
monitoring to use models from the explainable artificial intelligence field,
the evaluation still occurs mainly through performance-based metrics, thus not
accounting for the actionability and implications of the explanations. In this
paper, we define explainability through the interpretability of the
explanations and the faithfulness of the explainability model in the field of
process outcome prediction. The introduced properties are analysed along the
event, case, and control flow perspective which are typical for a process-based
analysis. This allows comparing inherently created explanations with post-hoc
explanations. We benchmark seven classifiers on thirteen real-life events logs,
and these cover a range of transparent and non-transparent machine learning and
deep learning models, further complemented with explainability techniques.
Next, this paper contributes a set of guidelines named X-MOP which allows
selecting the appropriate model based on the event log specifications, by
providing insight into how the varying preprocessing, model complexity and
explainability techniques typical in process outcome prediction influence the
explainability of the model.
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