Fairness-Aware Process Mining

• Fairness-aware process mining is a framework designed to identify, quantify, and mitigate discriminatory behaviors in process data.
• It applies metrics like demographic parity, equalized odds, and adversarial debiasing to improve model fairness while managing trade-offs with accuracy.
• Empirical studies using synthetic and real-world datasets demonstrate effective bias correction with human-in-the-loop and algorithmic mitigation strategies.

Fairness-aware process mining encompasses a suite of methodologies, metrics, and software interventions aimed at identifying, quantifying, and mitigating discriminatory behaviors in event logs and predictive models underlying business process management. As data-driven process mining and predictive monitoring become indispensable for operational support and organizational governance, the risk of perpetuating and amplifying systemic biases—particularly through sensitive attributes such as gender, age, and citizenship—has motivated the design of rigorous fairness-aware frameworks across algorithmic, interpretive, and human-in-the-loop paradigms (Möhrlein et al., 27 Aug 2025, Peeperkorn et al., 2024, Leoni et al., 2024, Käppel et al., 24 Aug 2025, Pohl et al., 2023, Qafari et al., 2019, Andreswari et al., 16 Jan 2026, Berti et al., 2023).

1. Formalization of Fairness in Process Mining

Fairness in process mining draws from classic group and individual fairness constructs, with special attention to the operational dynamics of event logs:

• Demographic Parity: Statistical independence of process outcomes from sensitive group membership. Formally, for binary protected attribute $S$:

$P(\hat{Y}=1\mid S=0) = P(\hat{Y}=1\mid S=1)$

The demographic parity gap, $\Delta_\mathrm{DP}$, measures the absolute difference.

• Equalized Odds and Opportunity: Equality of (true/false positive) rates across groups, conditioned on outcome ground truth.
• Procedural and Counterfactual Fairness: Respectively, ensures process steps (sequence, duration, reworks) do not systematically disadvantage groups (Berti et al., 2023), and that outcomes are unaffected by counterfactual alteration of sensitive traits.

Process mining scenarios instantiate fairness metrics in the context of activity prediction ($\Omega$: prefix $\to$ activity), outcome forecasting, or conformance checking, with features $X$ extracted from event log prefixes and target variables $Y$ set as next activity, time-to-completion, or categorical outcome (Käppel et al., 24 Aug 2025, Peeperkorn et al., 2024).

2. Fairness Metrics and Statistical Evaluation

Fairness-aware process mining leverages a suite of quantitative metrics, generally adapted from the fairness in machine learning literature:

• Demographic Parity Gap (DPG):

$$\Delta_\mathrm{DP} = \left|P(\hat{Y}=1 | S=s_1) - P(\hat{Y}=1 | S=s_2)\right|$$

• Equal Opportunity Gap (EOG):

$$\Delta_\mathrm{EO} = \left|P(\hat{Y}=1 | Y=1, S=s_1) - P(\hat{Y}=1 | Y=1, S=s_2)\right|$$

• Statistical Parity Difference (SPD), Disparate Impact (DI), Equal Opportunity Difference (EOD), Average Odds Difference (AOD): These are all computable directly using well-labeled simulated logs (Pohl et al., 2023).

Beyond thresholded group differences, distribution-based metrics such as area between probability-density (ABPC) or cumulative-density curves (ABCC) are deployed for threshold-independent fairness assessment:

$$\mathrm{ABPC} = \int_{0}^1 |f_0(u) - f_1(u)|\,du, \qquad \mathrm{ABCC} = \int_{0}^1 |F_0(u) - F_1(u)|\,du$$

where $(f_0, f_1)$ and $(F_0, F_1)$ denote respective PDFs and CDFs of prediction scores for groups $S=0,1$ (Peeperkorn et al., 2024).

Domain-specific process outcomes (processing time, number of reworks, conformance deviation) are statistically compared across sensitive groups using nonparametric tests (Kruskal–Wallis, $\epsilon^2$ effect size) for continuous, and chi-squared/Cramér’s $V$ for categorical decisions, as in triage fairness audit pipelines (Andreswari et al., 16 Jan 2026).

3. Bias Discovery and Mitigation Algorithms

3.1 Fair Classifiers for Process Mining

Initial frameworks post-process the output of classification trees to enforce an upper bound $\epsilon$ on discrimination. For decision tree $h_0$, the leaf relabeling problem is cast as a knapsack optimization:

$$\min_{L\subseteq \text{leaves}} \sum_{\ell\in L} \Delta\text{err}(\ell) \;\;\; \text{s.t.} \;\; \sum_{\ell\in L} \Delta\text{disc}(\ell) \geq \text{disc}(h_0) - \epsilon$$

with $\Delta\text{disc}(\ell)$ and $\Delta\text{err}(\ell)$ denoting discrimination and error change for each relabeling (Qafari et al., 2019). This method is implemented as a ProM plug-in.

3.2 Human-in-the-Loop Context-Aware Correction

Both FairLoop (Möhrlein et al., 27 Aug 2025) and related approaches (Käppel et al., 24 Aug 2025) introduce a pipeline leveraging model distillation:

• Train a complex neural predictor (MLP or similar).
• Distill its logic into a decision tree $D$.
• Domain experts review and surgically edit unfair S-based splits (discard, retrain, or re-grow subtrees).
• The modified logic is used to fine-tune the original predictor toward a "fairer" target (label relabeling).

This enables targeted, context-sensitive mitigation: the same sensitive feature may be allowed in fair medical routing decisions but forbidden in screening denials. Fine-tuning maintains original model capacity outside the excised unfair regions, achieving a Pareto-efficient balance of accuracy and fairness (Käppel et al., 24 Aug 2025).

3.3 Algorithmic Debiasing via Adversarial Learning

Adversarial frameworks embed a debiasing phase directly into neural predictors, appending an adversary network $g$ tasked with reconstructing the protected attribute $A$ from the hidden representation $h$ of the predictor $f$:

$$\min_{\theta_f} \max_{\theta_g} L_\text{pred}(\theta_f) - \lambda L_\text{adv}(\theta_f, \theta_g)$$

The gradient reversal layer ensures that $f$'s hidden space progressively loses information about $A$, while output accuracy on task $Y$ remains prioritized through $\lambda$ regularization (Leoni et al., 2024). This setup handles both regression and classification pipeline variants.

3.4 Distribution-Based Regularization

Composite loss functions combine predictive loss (BCE) and an integral probability metric (such as Wasserstein distance between group predictive distributions), enabling direct trade-off tuning between accuracy and fairness:

$$\mathcal{L}_\mathrm{total} = (1-\lambda) \mathcal{L}_\mathrm{BCE} + \lambda \mathcal{L}_\mathrm{IPM}$$

This method enforces threshold-independent group fairness and is empirically validated with LSTM architectures on synthetic process logs (Peeperkorn et al., 2024).

4. Simulated and Real-World Datasets for Fairness Benchmarking

A persistent impediment to systematic fairness research has been the scarcity of process event logs with known ground truth on sensitive attributes and embedded discrimination scenarios. The synthetic CPN log suite (Pohl et al., 2023) addresses this gap, providing XES-compliant logs across hiring, hospital, lending, and renting domains, annotated with discrimination level (low/med/high), protected group flags, and embedded process biases (via token guards on CPN transitions). This enables:

• Evaluation of discrimination metrics (DI, SPD, EOD, AOD) at various severity levels.
• Benchmarking of fairness-aware algorithms and intervention efficacy, supporting reproducible research protocols.

Event logs from empirical domains (e.g., MIMICEL for healthcare triage (Andreswari et al., 16 Jan 2026)) facilitate mapping of statistical disparities onto organizational justice dimensions.

5. Empirical Results and Trade-offs

Across both synthetic and real scenarios, the principal empirical observation is a Pareto trade-off between fairness (as measured by group discrimination differences or distributional parity) and predictive accuracy:

• Removing sensitive attributes often reduces but does not eliminate unfairness due to proxy features (Peeperkorn et al., 2024).
• Blanket removal may preclude legitimate, fair use of attributes in process-specific contexts (e.g., appropriate assignment of medical screening modes by gender).
• Human-in-the-loop context-aware approaches yield accuracy above the "no sensitive attributes" baseline while achieving near-zero demographic parity gaps ($\Delta DP \approx 0.002$) (Käppel et al., 24 Aug 2025).
• Adversarial debiasing typically results in a 2–8% drop in predictive accuracy for an order-of-magnitude decrease in the measured influence of protected features (as assessed by Shapley-value or equalized odds metrics) (Leoni et al., 2024).
• Distribution-matching regularization provides explicit control over the fairness–accuracy curve, allowing domain practitioners to select trade-off points appropriate for regulatory and operational contexts (Peeperkorn et al., 2024).
• LLM-based analysis can rapidly surface procedural disparities and group-sensitive control flow differences in event logs, but current techniques offer only qualitative "fairness debugging" rather than direct metric-based auditing (Berti et al., 2023).

6. Application Cases and Tooling

State-of-the-art fairness-aware process mining pipelines encompass:

• Healthcare: Automated triage analysis via the MIMICEL log, mapping clinical outcomes, deviation, and rework to distributive, procedural, and interactional justice dimensions, and quantifying disparities using nonparametric and effect-size statistics (Andreswari et al., 16 Jan 2026).
• Business Applications: Predictive process monitoring models for next-activity, outcome, and remaining-time tasks, integrating debiasing phases and expert-led repair for compliance with fairness requirements in regulated domains (Möhrlein et al., 27 Aug 2025, Käppel et al., 24 Aug 2025).
• Tool Support: ProM plug-ins for demographic parity enforcement (Qafari et al., 2019), browser-based human-in-the-loop editing (FairLoop (Möhrlein et al., 27 Aug 2025)), and programmatic pipelines in Python (pm4py, scikit-learn, TensorFlow).

A table of selected representative benchmarks and techniques appears below.

Methodology	Metric(s)	Domain(s) / Dataset
Post-processing relabeling (C4.5)	DP gap, accuracy	Receipt, Hospital bill
Human-in-loop distillation (NN+DT)	DP, context review	Simulated, loan, health
Adversarial debiasing (GRL/NN)	DP, EOD, Shapley	Incident, hospital, hiring
Distribution-matching (BCE+IPM)	ABPC, ABCC, AUC	Synthetic hiring/lending
LLM-based procedural inspection	qualitative, confusion-matrix	Simulated logs

7. Limitations and Future Trajectories

Despite substantive advances, challenges persist:

• Automated context-sensitive detection of equity violations remains nascent; current pipelines often require expert-in-the-loop validation (Möhrlein et al., 27 Aug 2025, Käppel et al., 24 Aug 2025).
• Hyperparameter and encoding flexibility in fairness intervention toolkits is limited relative to standard AutoML pipelines.
• Fairness metric computation is not fully integrated in interactive UIs; most interventions rely on post-hoc analysis.
• No large-scale in-field user studies as of 2026—further work is needed to measure real-world usability and cost–benefit.
• Addressing multi-label, temporal, or resource-level fairness (especially in multi-actor or object-centric process mining) remains open.

Future work prioritizes extending intervention frameworks to outcome/remaining-time prediction (Möhrlein et al., 27 Aug 2025, Käppel et al., 24 Aug 2025), integrating fairness constraints at all BPML lifecycle stages (Andreswari et al., 16 Jan 2026), developing counterfactual and individual fairness testing (Berti et al., 2023), and deploying real-time fairness dashboards for monitoring organizational processes in situ.

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Fairness-aware process mining has matured into an empirically grounded, technically nuanced discipline, blending algorithmic, interpretive, and operational approaches to address equity concerns in evolving process-analytic settings (Möhrlein et al., 27 Aug 2025, Peeperkorn et al., 2024, Leoni et al., 2024, Käppel et al., 24 Aug 2025, Andreswari et al., 16 Jan 2026). The availability of well-annotated logs, formalized metrics, and interactive tooling underpins both reproducible research and compliant organizational deployment.