iFlipper: Label Flipping for Individual Fairness
Summary: iFlipper uses label flipping as pre-processing to enforce individual fairness by minimizing flips within a bound on violations among similar instances. NP-hard; an approximate LP with guarantees yields near-optimal flips; optimizations boost fairness/accuracy and it outperforms pre-processing baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hantian Zhang (Georgia Institute of Technology)
- 2. Ki Hyun Tae (Korea Advanced Institute of Science and Technology)
- 3. Jaeyoung Park (Korea Advanced Institute of Science and Technology)
- 4. Xu Chu (Georgia Institute of Technology)
- 5. Steven Euijong Whang (Korea Advanced Institute of Science and Technology)
BibTeX Citation
@inproceedings{zhang_sigmod23,
title = {{iFlipper: Label Flipping for Individual Fairness}},
author = {Zhang, Hantian and Tae, Ki Hyun and Park, Jaeyoung and Chu, Xu and Whang, Steven Euijong},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588688},
url = {https://dl.acm.org/doi/10.1145/3588688},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,043 | Falcon: Fair Active Learning using Multi-armed Bandits | 2024 | VLDB | 5.5013766e-05 |
| 9,773 | Fair and Actionable Causal Prescription Ruleset | 2025 | SIGMOD | 5.2209769e-05 |
| 10,239 | Fair Data Pre-Processing with Imperfect Attribute Space | 2026 | SIGMOD | 5.093636e-05 |
| 10,511 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 5.093636e-05 |
| 10,757 | Data Enhancement for Binary Classification of Relational Data | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025235185 |
| 863 | Interventional Fairness : Causal Database Repair for Algorithmic Fairness | 2019 | SIGMOD | 0.00013531835 |
| 3,701 | Snorkel: Fast Training Set Generation for Information Extraction | 2017 | SIGMOD | 7.185321e-05 |
| 4,658 | OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning | 2021 | SIGMOD | 6.5817368e-05 |
| 5,334 | Operationalizing Individual Fairness with Pairwise Fair Representations | 2020 | VLDB | 6.2622047e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,709 | Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification | 2022 | SIGMOD |
| 2 | 8,043 | Falcon: Fair Active Learning using Multi-armed Bandits | 2024 | VLDB |
| 3 | 6,637 | Causal Feature Selection for Algorithmic Fairness | 2022 | SIGMOD |
| 4 | 10,239 | Fair Data Pre-Processing with Imperfect Attribute Space | 2026 | SIGMOD |
| 5 | 9,220 | Satisfying Complex Top-k Fairness Constraints by Preference Substitutions | 2023 | VLDB |
| 6 | 5,489 | Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching | 2023 | VLDB |
| 7 | 5,334 | Operationalizing Individual Fairness with Pairwise Fair Representations | 2020 | VLDB |
| 8 | 3,905 | Automated Feature Engineering for Algorithmic Fairness | 2021 | VLDB |
| 9 | 863 | Interventional Fairness : Causal Database Repair for Algorithmic Fairness | 2019 | SIGMOD |
| 10 | 4,658 | OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning | 2021 | SIGMOD |