Summary of Generating Feasible and Plausible Counterfactual Explanations For Outcome Prediction Of Business Processes, by Alexander Stevens et al.
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
by Alexander Stevens, Chun Ouyang, Johannes De Smedt, Catarina Moreira
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to generating counterfactual explanations for predictive process analytics models. Counterfactuals provide insights into the decision-making process behind undesirable predictions, making them more understandable for human decision-makers. The existing approaches face challenges when dealing with sequential data, which is common in business process analytics. To overcome this challenge, the authors propose a data-driven approach called REVISEDplus that generates feasible and plausible counterfactual explanations by restricting the algorithm to generate counterfactuals within a high-density region of the process data distribution. The approach also learns sequential patterns between activities using Declare language templates. The paper evaluates the validity of the generated counterfactuals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machine learning models more understandable for people. Right now, these models are really good at making predictions, but they don’t explain why they made those predictions. This makes it hard for humans to trust the model’s decisions. The authors introduce a new way to generate “what if” scenarios that show how things could have turned out differently. These counterfactual explanations can help people understand why the model made certain decisions. The challenge is that most data is sequential, like a series of steps in a business process. To overcome this challenge, the authors propose a new approach that looks at patterns in the data to generate more realistic and plausible “what if” scenarios. |
Keywords
» Artificial intelligence » Machine learning
