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)

Paper ID: 12253
Venue: VLDB
Year: 2020
Pagerank: 5.523874e-05
Overall Rank: 5,407 | 62.43%
DOI: 10.14778/3372716.3372723

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,850	Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification	2022	SIGMOD	4.9036077e-05
8,058	iFlipper: Label Flipping for Individual Fairness	2023	SIGMOD	4.5903348e-05
9,373	Falcon: Fair Active Learning using Multi-armed Bandits	2024	VLDB	4.3460825e-05

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
1,037	Interventional Fairness : Causal Database Repair for Algorithmic Fairness	2019	SIGMOD	0.00014514825
1,577	Designing Fair Ranking Schemes	2019	SIGMOD	0.00011276814

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