Fairness in Prescriptive Process Analytics: Integrating Fairness Constraints into the Learning Phase

Fabrizio De Santis, Alessandro Padella, Gyunam Park, Francesco Zanichelli, Massimiliano De Leoni
September, 2026
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Abstract

Prescriptive Process Analytics is a core field in business process management that provides automated activity recommendations to improve Key Performance Indicators in business processes. Because such recommendations can directly affect individuals involved in the process, ensuring fairness is essential. Recent work has begun to address fairness in process monitoring by enforcing group-level parity across demographic groups at the predictive stage. However, these approaches leave the prescriptive phase entirely unexamined and rely solely on group fairness notions, failing to account for individual-level counterfactual fairness (i.e., the requirement that a recommendation should not change when sensitive attributes are altered). To address this gap, we propose to enforce counterfactual fairness directly in prescriptive process monitoring. We develop a novel neuro-symbolic framework based on Logic Tensor Networks that encodes counterfactual fairness as a logical axiom over predictions, seamlessly integrated into the learning process. We evaluate our approach on real-world event logs and find that it enforces counterfactual fairness at the prediction level and improves recommendation-level counterfactual consistency under the considered policy, while maintaining competitive predictive and prescriptive performance, establishing the first fairness-aware framework for Prescriptive Process Analytics.

Type
Conference paper
Publication
Business Process Management

Practical problem

Companies increasingly let software recommend the next step in a running case, such as whether a loan application goes to manual review or a fast track, or which department a patient is sent to. Those recommendations are learned from historical data, so they can quietly depend on gender, age, or nationality, and the person affected has no way of knowing. The fairness tools that exist for process analytics work only on the prediction side and only check that groups look equal on average, which still allows an individual to receive a different recommendation purely because of a protected attribute. Process owners and compliance teams therefore lack a way to guarantee that a given case would be treated the same if only its sensitive attribute were different.

Research questions

  1. How can counterfactual fairness, invariance of predictions and recommendations under a change of the sensitive attribute, be enforced in prescriptive process analytics rather than only in predictive monitoring?
  2. Does enforcing counterfactual fairness at the prediction level carry over to the recommendation level, and at what cost in predictive accuracy and recommendation effectiveness?

Contributions

  • A counterfactual event-log generation procedure that uses Fast Causal Inference (FCI) to discover causal dependencies among case-level attributes and fits structural equations to intervene on a sensitive attribute consistently.
  • A neuro-symbolic prescriptive framework (Fair-LTN) built on Logic Tensor Networks that jointly optimizes a regression axiom for key performance indicator (KPI) accuracy and a counterfactual fairness axiom.
  • Two evaluation metrics for fairness in process analytics: Counterfactual Mean Squared Error (CF-MSE) at prediction level and Counterfactual Recommendation Score (CF-RECC) at recommendation level.
  • A simulation-based evaluation protocol using the ProSiT simulator on original and counterfactual test logs, with code released on GitHub (FabrizioDeSantis/Fair-PrescriptiveProcessMonitoring).
  • An empirical study on BPI Challenge 2012, three Hospital Billing scenarios, and BPI Challenge 2013 incidents with gender, age, and organization country as sensitive attributes.

Key findings

  • Fair-LTN keeps or improves predictive accuracy: on BPI Challenge 2012 its mean absolute error for remaining time is 12.46 days versus 13.08 for a plain LSTM and 12.64 for an LTN without the fairness axiom.
  • Prediction-level fairness improves sharply: CF-MSE on Hospital Billing with gender bias falls from 41.53 (LSTM) and 42.20 (LTN-Data) to 2.85, and on BPI Challenge 2013 from 265.76 and 199.71 to 11.83.
  • Where the sensitive attribute changes the ranking of candidate activities, recommendation consistency recovers: CF-RECC rises from 0.77 to 0.94 on Hospital Billing with gender bias and from 0.68 to 0.85 on BPI Challenge 2013.
  • Fairness costs little effectiveness: simulated trace durations under Fair-LTN stay within 0.3 days of the unfair baselines on the BPI logs and within 1.7 days on Hospital Billing, where all methods cut duration by 33-56% and 3-7% respectively.
  • The carry-over to prescriptive fairness holds only for a policy ranking activities by predicted KPI with a fair transition system; where few candidate activities exist, unfair baselines already exceed 0.95 CF-RECC, and results depend on the discovered causal graph.