Extending LIME for Business Process Automation
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Abstract (arXiv)
AI business process applications automate high-stakes business decisions where there is an increasing demand to justify or explain the rationale behind algorithmic decisions. Business process applications have ordering or constraints on tasks and feature values that cause lightweight, model-agnostic, existing explanation methods like LIME to fail. In response, we propose a local explanation framework extending LIME for explaining AI business process applications. Empirical evaluation of our extension underscores the advantage of our approach in the business process setting.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 9 Aug 2021.
Builds on
11 of 24 references.
References · 11
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2016The Mythos of Model Interpretabilitycited by 716
- 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
- 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
- 2020Problems with Shapley-value-based explanations as feature importance measurescited by 198
- 2016Predictive Business Process Monitoring with LSTM Neural Networkscited by 103
- 2016Predicting Process Behaviour using Deep Learningcited by 59
- 2020Robust and Stable Black Box Explanationscited by 47
- 2020Model Agnostic Multilevel Explanationscited by 24
- 2020AI Trust in business processes: The need for process-aware explanationscited by 7
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1.
3 papers.