Research map / Learning theory and algorithms / Fairness
Fairness and Bias in Online Selection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fair prophet algorithms (Fair PA / Fair IID) vs unfair baselines (SC, EHKS, CFHOV, DP) | multi-color prophet problem · i.i.d. prophet, uniform and binomial streams | selection fairness across stream positions | Helped |
| Fair IID prophet algorithm vs Fair PA algorithm | multi-color prophet problem · i.i.d. uniform stream (50 samples) | fraction of optimal value: 88.01% vs 66.71% | Helped |
| Fair secretary algorithm vs single-color secretary algorithm (SCSA) | multi-color secretary problem · synthetic equal p | number of elements picked: +30.5% | Helped |
| Fair secretary algorithm vs SA (classic secretary ignoring colors) | multi-color secretary problem · synthetic equal p and real-world datasets | color selection balance | Helped |
| Fair secretary algorithm vs SCSA on general p | multi-color secretary problem · synthetic general p | picks of maximum element of picked color: +63.0% | Helped |
| Fair secretary algorithm vs SCSA on feedback maximization | multi-color secretary problem · feedback maximization (bank calls) | times picking color max: +76.0% | Helped |
| Fair secretary algorithm vs SCSA on influence maximization | multi-color secretary problem · influence maximization (Pokec) | times picking color max: +75.6% | Helped |
| Fair IID prophet algorithm vs DP (optimal unfair threshold algorithm) | multi-color prophet problem · i.i.d. uniform stream | average value of chosen candidate: 0.661 vs 0.751 | Hurt |
| Using optimal single threshold (equal probabilities) in secretary problem | multi-color secretary problem · equal p, k colors | competitive ratio | No clear effect |
Closest papers by meaning
- Addressing Bias in Online Selection with Limited Budget of Comparisons (NeurIPS 2024 · similarity 0.88)
- Fair Secretaries with Unfair Predictions (NeurIPS 2024 · similarity 0.87)
- Fairness Shields: Safeguarding against Biased Decision Makers (AAAI 2025 · similarity 0.87)
- Identifying Reasons for Bias: An Argumentation-Based Approach (AAAI 2024 · similarity 0.86)
- FairJob: A Real-World Dataset for Fairness in Online Systems (NeurIPS 2024 · similarity 0.86)
- Reconciling Predictive and Statistical Parity: A Causal Approach (AAAI 2024 · similarity 0.86)
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