Explainable Predictive Process Monitoring: A User Evaluation
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arXiv
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| Format: | Preprint |
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2022
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| author | Rizzi, Williams Comuzzi, Marco Di Francescomarino, Chiara Ghidini, Chiara Lee, Suhwan Maggi, Fabrizio Maria Nolte, Alexander |
| author_facet | Rizzi, Williams Comuzzi, Marco Di Francescomarino, Chiara Ghidini, Chiara Lee, Suhwan Maggi, Fabrizio Maria Nolte, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_07760 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Explainable Predictive Process Monitoring: A User Evaluation Rizzi, Williams Comuzzi, Marco Di Francescomarino, Chiara Ghidini, Chiara Lee, Suhwan Maggi, Fabrizio Maria Nolte, Alexander Artificial Intelligence 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. |
| title | Explainable Predictive Process Monitoring: A User Evaluation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2202.07760 |