Pith. sign in

REVIEW 1 cited by

A Collection of Simulated Event Logs for Fairness Assessment in Process Mining

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2306.11453 v1 pith:JJC6UQB5 submitted 2023-06-20 cs.DB

classification cs.DB
keywords fairnesseventlogsminingprocessanalysiscollectiondata
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The analysis of fairness in process mining is a significant aspect of data-driven decision-making, yet the advancement in this field is constrained due to the scarcity of event data that incorporates fairness considerations. To bridge this gap, we present a collection of simulated event logs, spanning four critical domains, which encapsulate a variety of discrimination scenarios. By simulating these event logs with CPN Tools, we ensure data with known ground truth, thereby offering a robust foundation for fairness analysis. These logs are made freely available under the CC-BY-4.0 license and adhere to the XES standard, thereby assuring broad compatibility with various process mining tools. This initiative aims to empower researchers with the requisite resources to test and develop fairness techniques within process mining, ultimately contributing to the pursuit of equitable, data-driven decision-making processes.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Achieving Group Fairness through Independence in Predictive Process Monitoring

    cs.LG 2024-12 conditional novelty 5.0 of 10

    A proof-of-concept that transfers distribution-level demographic parity metrics and a Wasserstein composite loss to outcome-oriented predictive process monitoring, with a tunable accuracy-fairness trade-off.

Pith tools