Research map / Learning theory and algorithms / Fairness
FACT: A Diagnostic for Group Fairness Trade-offs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FACT/MS-LAFOP for post-processing instead of the EOd post-processing algorithm | Adult classifiers · Adult | accuracy | Helped |
| Use FACT/MS-LAFOP for post-processing instead of the EOd post-processing algorithm | Adult classifiers · Adult | fairness gap | Helped |
| Add demographic parity (DP) to equalized odds (EOd) | LAFOP · S(U), S(B) | accuracy: over 60 percent | Hurt |
| Add CB, PE, and/or PCB to EOd and DP | LAFOP · S(U), S(B) | accuracy | No clear effect |
| Impose DP fairness | LAFOP · S(U) | accuracy | Hurt |
| Impose EOd fairness | LAFOP · S(U), S(B) | accuracy | Hurt |
| Increase PCB regularization while holding EOd and DP strengths fixed | MLAFOP · S(B) | accuracy | No clear effect |
| Increase DP regularization while holding PCB and EOd strengths fixed | MLAFOP · S(B) | accuracy | Hurt |
| Increase EOd regularization while holding PCB and DP strengths fixed | MLAFOP · S(B) | accuracy | Hurt |
| Use the FGP fair-classification model rather than the other plotted fair models | Fair classifiers · Adult | accuracy-fairness trade-off | Helped |
Closest papers by meaning
- Testing Group Fairness via Optimal Transport Projections (ICML 2021 · similarity 0.89)
- Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness (NeurIPS 2025 · similarity 0.89)
- Enhancing Group Fairness in Online Settings Using Oblique Decision Forests (ICLR 2024 · similarity 0.89)
- Rethinking Pareto Frontier: On the Optimal Trade-offs in Fair Classification (ICLR 2026 · similarity 0.89)
- FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods (ICLR 2024 · similarity 0.88)
- Fairness without Demographics through Shared Latent Space-Based Debiasing (AAAI 2024 · similarity 0.87)
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