Operationalizing Individual Fairness with Pairwise Fair Representations
Summary: Operationalizes individual fairness without a human distance metric by learning a unified Pairwise Fair Representation (PFR) that fuses data-driven similarity with a fairness-graph of equally deserving pairs. Elicits judgments from diverse sources (COMPAS, Crime & Communities), demonstrating practical viability of the approach. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,046 | Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification | 2022 | SIGMOD | 4.8525913e-05 |
| 8,055 | iFlipper: Label Flipping for Individual Fairness | 2023 | SIGMOD | 4.5947404e-05 |
| 9,365 | Falcon: Fair Active Learning using Multi-armed Bandits | 2024 | VLDB | 4.3502315e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,041 | Interventional Fairness : Causal Database Repair for Algorithmic Fairness | 2019 | SIGMOD | 0.00014482047 |
| 1,597 | Designing Fair Ranking Schemes | 2019 | SIGMOD | 0.00011209846 |
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