Research map / Learning theory and algorithms / Fairness
Censored Fairness through Awareness
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the censored individual fairness loss to DeepSurv | DeepSurv · ROSSI | DCF@10%: 31.43 -> 57.68 | Helped |
| Add the censored individual fairness loss to DeepSurv | DeepSurv · COMPAS | DCF@10%: 74.18 -> 86.78 | Helped |
| Add the censored individual fairness loss to DeepSurv | DeepSurv · KKBOX | DCF@10%: 43.45 -> 69.64 | Helped |
| Add the censored individual fairness loss to DeepSurv | DeepSurv · ROSSI | C-index%: 66.67 -> 65.87 | Hurt |
| Add the censored individual fairness loss to DeepSurv | DeepSurv · COMPAS | C-index%: 75.12 -> 73.17 | Hurt |
| Add the censored individual fairness loss to DeepSurv | DeepSurv · KKBOX | C-index%: 83.01 -> 84.15 | Helped |
| Add the censored individual fairness loss to DeepSurv | DeepSurv · ROSSI | Brier Score%: 14.71 -> 15.05 | Hurt |
| Add the censored individual fairness loss to DeepSurv | DeepSurv · COMPAS | Brier Score%: 13.42 -> 13.89 | Hurt |
| Add the censored individual fairness loss to DeepSurv | DeepSurv · KKBOX | Brier Score%: 14.33 -> 14.35 | Hurt |
| Increase the fairness weight λ (ablation) | IFS · ROSSI, COMPAS, and KKBOX | DCF@10% | Mixed |
| Increase the fairness weight λ (ablation) | IFS · ROSSI, COMPAS, and KKBOX | model utility | Hurt |
| Use a larger top-k ranking list (ablation) | IFS · ROSSI, COMPAS, and KKBOX | DCF@10% | Helped |
Closest papers by meaning
- Fairness with Censorship: Bridging the Gap between Fairness Research and Real-World Deployment (AAAI 2024 · similarity 0.93)
- Generalized Disparate Impact for Configurable Fairness Solutions in ML (ICML 2023 · similarity 0.89)
- Reconciling Predictive and Statistical Parity: A Causal Approach (AAAI 2024 · similarity 0.87)
- Fairness-Accuracy Trade-Offs: A Causal Perspective (AAAI 2025 · similarity 0.87)
- Fairness Shields: Safeguarding against Biased Decision Makers (AAAI 2025 · similarity 0.86)
- Individual Fairness In Strategic Classification (NeurIPS 2025 · similarity 0.86)
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