Published December 1, 2025 | Version v1
Dataset Open

Shortlisted CVs

Authors/Creators

  • 1. Max Planck Institute for Security and Privacy
  • 2. ROR icon University of Trieste
  • 3. ROR icon University of Amsterdam
  • 4. ROR icon Pompeu Fabra University
  • 5. Catalan Institution for Research and Advanced Studies (ICREA)

Description

This repository accompanies the paper:

Alessandro Fabris, Clara Rus, Jorge Saldivar, Anna Gatzioura, Asia J. Biega, Carlos Castillo,
Does fair ranking lead to fair recruitment outcomes? A study of interventions, interfaces, and interactions,
Information Processing & Management,
2026,
https://doi.org/10.1016/j.ipm.2025.104506.

 

This project investigates whether fair ranking algorithms actually produce fair recruitment outcomes when humans make final decisions.  The data combines:

  • Job descriptions
  • Carefully designed candidate profiles (constructed to manipulate demographic cues and job-relevant skills)
  • Algorithmic interventions (e.g., fitness-based, discriminatory, and compensatory rankings)
  • Human shortlisting decisions (participants selecting candidates from ranked lists)

More info in README files

Files

fair_recruitment.zip

Files (1.6 MB)

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Additional details

Software

Programming language
Python

References

  • Alessandro Fabris, Clara Rus, Jorge Saldivar, Anna Gatzioura, Asia J. Biega, Carlos Castillo, Does fair ranking lead to fair recruitment outcomes? A study of interventions, interfaces, and interactions, Information Processing & Management, Volume 63, Issue 3, 2026, 104506, ISSN 0306-4573, https://doi.org/10.1016/j.ipm.2025.104506.
  • Clara Rus, Gabrielle Poerwawinata, Andrew Yates, and Maarten de Rijke. AnnoRank: A Comprehensive Web-Based Framework for Collecting Annotations and Assessing Rankings. 2024. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24). Association for Computing Machinery, New York, NY, USA, 5400–5404. https://doi.org/10.1145/3627673.3679174