Improving Fairness with Ensemble Combination: Margin-Dependent Bounds

arXiv:2301.10813 · cs.LG, cs.AI, cs.CY · Submitted 2023-01-25 · Read on arXiv

cs.LG, cs.AI, cs.CY

Submitted: 2023-01-25

Updated: 2026-03-11

Comments: Accepted by ACM FAccT 2026. Code is available on https://github.com/eustomaqua/FairML

DOI: 10.1145/3805689.3806539

Code: https://github.com/eustomaqua/FairML

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Pruning — 78× in this paper · explained in 4 episodes
• Accuracy — 70× in this paper · explained in 1 episode
• Objective Function — 9× in this paper · explained in 2 episodes
• F1 Score — 6× in this paper · explained in 3 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Theoretical Guarantees — 3× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Contextual Bandits — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Penalizing Unfairness in Binary Classification
• FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling

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