Summary of Achieving Fairness in Predictive Process Analytics Via Adversarial Learning (extended Version), by Massimiliano De Leoni et al.
Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)
by Massimiliano de Leoni, Alessandro Padella
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 Predictive business process analytics has become crucial for organizations, offering real-time operational support for their processes. However, these algorithms often perform unfair predictions because they are based on biased variables (e.g., gender or nationality), namely variables embodying discrimination. This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics to ensure that predictions are not influenced by biased variables. Our framework leverages adversarial debiasing and is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value. The proposed technique is also compared with the state of the art in fairness in process mining, illustrating that our framework allows for a more enhanced level of fairness, while retaining a better prediction quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive business process analytics helps companies make decisions quickly and effectively. However, these algorithms can be unfair because they use information that is biased against certain groups, like gender or nationality. This paper shows how to add a “debiasing” step to predictive business process analytics to ensure the predictions are fair and unbiased. We tested our method on four real-life cases and found it significantly reduced the impact of biased information. Our approach is better than current methods at achieving fairness while still making accurate predictions. |
