Measuring Model-Induced Discrimination via Efficient Fairness Approximation

arXiv:2405.09251 · cs.LG, cs.CY · Submitted 2024-05-15 · Read on arXiv

cs.LG, cs.CY

Submitted: 2024-05-15

Updated: 2026-06-21

Comments: Accepted by TNNLS. These two authors contributed equally

DOI: 10.1109/TNNLS.2026.3706648

Code: https://github.com/propublica/compas-analysis

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

Terminology

• Accuracy — 19× in this paper · explained in 1 episode
• F1 Score — 6× in this paper · explained in 3 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Causal Inference — 1× in this paper · explained in 4 episodes
• Contextual Bandits — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distribution Matching — 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
• Intersectionality — 1× in this paper · explained in 2 episodes
• Intrinsic Dimension — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• Procedural Fairness in Machine Learning

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