Research map / Learning theory and algorithms / Fairness
Adapting Fairness Interventions to Missing Values
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add missing indicators to the fair classifier instead of impute-then-classify | FairProjection logistic regression · COMPAS and IPUMS Adult, MNAR missingness | fairness-accuracy tradeoff | Helped |
| add missing indicators or affinely adaptive classification | FairProjection logistic regression · COMPAS and IPUMS Adult, MCAR and MAR missingness | fairness-accuracy tradeoff | No clear effect |
| add missing indicators | DispMistreatment · HSLS | fairness-accuracy tradeoff | Helped |
| use affinely adaptive classification | DispMistreatment · HSLS | accuracy vs fairness | Mixed |
| use the three adaptive methods instead of impute-then-classify | FairProjection · HSLS | fairness-accuracy tradeoff | Helped |
| use missing pattern clustering instead of impute-then-classify | DispMistreatment and FairProjection · synthetic data | accuracy and discrimination | Helped |
| add missing indicators | FairProjection · synthetic data | fairness-accuracy tradeoff | No clear effect |
| add missing indicators | DispMistreatment · synthetic data | fairness-accuracy tradeoff | Helped |
| add missing indicators | Reduction, ROC, Leveraging, EqOdds · HSLS | fairness-accuracy tradeoff | Mixed |
| use FairMissBag ensemble instead of impute-then-classify | Reduction with random forest · HSLS, KNN and iterative imputation | fairness-accuracy tradeoff | Helped |
| use FairMissBag ensemble | EqOdds with random forest · HSLS, mean and KNN imputation | fairness-accuracy tradeoff | Helped |
| use FairMissBag ensemble | FairProjection with random forest · HSLS | accuracy vs fairness risk | Mixed |
Closest papers by meaning
- Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values (AAAI 2022 · similarity 0.93)
- The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective (AAAI 2021 · similarity 0.89)
- Assessing Fairness in the Presence of Missing Data (NeurIPS 2021 · similarity 0.89)
- Demystifying the Optimal Fair Classifier in Multi-Class Classification (ICML 2026 · similarity 0.88)
- FRAPPÉ: A Group Fairness Framework for Post-Processing Everything (ICML 2024 · similarity 0.87)
- How Far Can Fairness Constraints Help Recover From Biased Data? (ICML 2024 · similarity 0.87)
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