Abstract
Explainable Predictive Process Monitoring aims at deriving explanations of the inner workings of black-box classifiers used to predict the continuation of ongoing process executions. Most existing techniques use data attributes (e.g., the loan amount) to explain the prediction outcomes. However, explanations based on control flow patterns (such as calling the customers first, and then validating the application, or providing early discounts) cannot be provided. This omission may result in many valuable, actionable explanations going undetected. To fill this gap, this paper proposes PABLO (PAttern Based LOcal Explanations), a framework that generates local control-flow aware explanations for a given predictive model. Given a process execution and its outcome prediction, PABLO discovers control-flow patterns from a set of alternative executions, which are used to deliver explanations that support or flip the prediction for the given process execution. Evaluation against real-life event logs shows that PABLO provides high-quality explanations of predictions in terms of fidelity and accurately explains the reasoning behind the predictions of the black box models. A qualitative comparison showcases how the patterns that PABLO derives can influence the prediction outcome, aligned with the early findings from the literature.
This work was partially supported by the Italian (MUR) under PRIN project PINPOINT Prot. 2020FNEB27, CUP H23C22000280006 and H45E21000210001 and PNRR project FAIR-Future AI Research (PE00000013), under the NRRP MUR program funded by NextGenerationEU.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Hyperparameter optimisation was done by using the hyperopt python library.
- 3.
Link to the report of the challenger winners.
References
Buliga, A., Di Francescomarino, C., Ghidini, C., Maggi, F.M.: Counterfactuals and ways to build them: evaluating approaches in predictive process monitoring. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds.) CAiSE 2023. LNCS, vol. 13901, pp. 558–574. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34560-9_33
Elkhawaga, G., Abu-Elkheir, M., Reichert, M.: Explainability of predictive process monitoring results: can you see my data issues? Appl. Sci. 12(16), 8192 (2022)
Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., Turini, F.: Factual and counterfactual explanations for black box decision making. IEEE Intell. Syst. 34(6), 14–23 (2019)
Harl, M., Weinzierl, S., Stierle, M., Matzner, M.: Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst. 29(sup1), 312–327 (2020)
Hsieh, C., Moreira, C., Ouyang, C.: DiCE4EL: interpreting process predictions using a milestone-aware counterfactual approach. In: 2021 3rd International Conference on Process Mining (ICPM), pp. 88–95 (2021)
Huang, T., Metzger, A., Pohl, K.: Counterfactual explanations for predictive business process monitoring. In: Themistocleous, M., Papadaki, M. (eds.) EMCIS 2021. LNBIP, vol. 437, pp. 399–413. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-95947-0_28
Hundogan, O., Lu, X., Du, Y., Reijers, H.A.: CREATED: generating viable counterfactual sequences for predictive process analytics. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds.) CAiSE 2023. LNCS, vol. 13901, pp. 541–557. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34560-9_32
Mehdiyev, N., Fettke, P.: Explainable artificial intelligence for process mining: a general overview and application of a novel local explanation approach for predictive process monitoring. In: Pedrycz, W., Chen, S.-M. (eds.) Interpretable Artificial Intelligence: A Perspective of Granular Computing. SCI, vol. 937, pp. 1–28. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64949-4_1
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607–617 (2020)
Rizzi, W., Di Francescomarino, C., Maggi, F.M.: Explainability in predictive process monitoring: when understanding helps improving. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 141–158. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58638-6_9
Schönig, S., Di Ciccio, C., Maggi, F.M., Mendling, J.: Discovery of multi-perspective declarative process models. In: Sheng, Q.Z., Stroulia, E., Tata, S., Bhiri, S. (eds.) ICSOC 2016. LNCS, vol. 9936, pp. 87–103. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46295-0_6
Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 1–57 (2019)
Vazifehdoostirani, M., Genga, L., Lu, X., Verhoeven, R., van Laarhoven, H., Dijkman, R.: Interactive multi-interest process pattern discovery. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds.) BPM 2023. LNCS, vol. 14159, pp. 303–319. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-41620-0_18
Wickramanayake, B., Ouyang, C., Xu, Y., Moreira, C.: Generating multi-level explanations for process outcome predictions. Eng. Appl. Artif. Intell. 125, 106678 (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Buliga, A. et al. (2024). Uncovering Patterns for Local Explanations in Outcome-Based Predictive Process Monitoring. In: Marrella, A., Resinas, M., Jans, M., Rosemann, M. (eds) Business Process Management. BPM 2024. Lecture Notes in Computer Science, vol 14940. Springer, Cham. https://doi.org/10.1007/978-3-031-70396-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-70396-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70395-9
Online ISBN: 978-3-031-70396-6
eBook Packages: Computer ScienceComputer Science (R0)