用对抗学习消除流程分析中的偏见变量影响，提升预测公平性。
Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)
- 通过对抗学习构建去偏模块，抑制性别、国籍等敏感变量干扰
- 在四个案例中显著降低偏见变量对预测结果的贡献度
- 兼顾公平性与预测精度，适合注重伦理合规的工业流程系统
预测性业务流程分析对组织运营至关重要，但现有算法常因依赖性别、国籍等带偏见变量而产生不公平预测。本文提出一种集成去偏机制的框架，利用对抗学习减少敏感变量对预测结果的影响。在四个真实案例研究中验证，该方法显著降低偏见变量对预测值的贡献。相比当前流程挖掘中的公平性方法，本框架在提升公平性的同时保持更优的预测性能。
原文摘要 · Abstract (English)
Predictive business process analytics has become important 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 on adversial debiasing 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.
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