Auditing for Demographic Bias in Opaque Rankings
Summary: CONDOR audits demographic influence in rankings under strict black-box access, without querying rankers or observing latent scores. Kernel residualization plus partial distance correlation captures nonlinear conditional dependence, yielding normalized effect sizes and tests for protected-attribute influence. (summarized by gpt-5.6-luna on Aug 17 2026)
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Authors
- 1. Antonio Ferrara (Intesa Sanpaolo)
- 2. Carlo Abrate (Intesa Sanpaolo)
- 3. Fabio Vitale (Intesa Sanpaolo)
- 4. Francesco Bonchi (Intesa Sanpaolo)
BibTeX Citation
@article{ferrara_vldb26,
title = {{Auditing for Demographic Bias in Opaque Rankings}},
author = {Ferrara, Antonio and Abrate, Carlo and Vitale, Fabio and Bonchi, Francesco},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {9},
pages = {2140--2153},
doi = {10.14778/3819518.3819540},
url = {https://doi.org/10.14778/3819518.3819540},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,379 | Designing Fair Ranking Schemes | 2019 | SIGMOD | 0.0001086418 |
| 13,808 | The Responsibility Challenge for Data | 2019 | SIGMOD | - |
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