Scholarlink

Predictive process monitoring (PPM) faces fairness issues due to biases in historical data, causing discriminatory practices. Balancing fairness and performance in PPM is crucial but underexplored, possibly requiring the adaptation of ML fairness techniques to process mining. This study assesses Reweighing, Adversarial Debiasing, and Equalized Odds Postprocessing to reduce discrimination in PPM models and understand their trade-offs. Using synthetic event logs of a hiring process with varying discrimination levels, we analyzed the models’ performance and fairness metrics. Reweighing improved fairness with minimal performance loss, Adversarial Debiasing greatly boosted fairness but reduced accuracy and recall, and Equalized Odds Postprocessing kept performance without notable fairness gains. Our study offers insights into applying fairness techniques in PPM, advancing equitable and effective predictive models.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Towards Fairness-Aware Predictive Process Monitoring: Evaluating Bias Mitigation Techniques

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Mickaelle Caldeira da Silva,
  • Marcelo Fantinato,
  • Sarajane Marques Peres

摘要

Predictive process monitoring (PPM) faces fairness issues due to biases in historical data, causing discriminatory practices. Balancing fairness and performance in PPM is crucial but underexplored, possibly requiring the adaptation of ML fairness techniques to process mining. This study assesses Reweighing, Adversarial Debiasing, and Equalized Odds Postprocessing to reduce discrimination in PPM models and understand their trade-offs. Using synthetic event logs of a hiring process with varying discrimination levels, we analyzed the models’ performance and fairness metrics. Reweighing improved fairness with minimal performance loss, Adversarial Debiasing greatly boosted fairness but reduced accuracy and recall, and Equalized Odds Postprocessing kept performance without notable fairness gains. Our study offers insights into applying fairness techniques in PPM, advancing equitable and effective predictive models.