A Rank Graduation metric for Algorithmic fairness

arXiv:2609.39025 · cs.LG, stat.AP, stat.ML · Submitted 2026-09-30 · Read on arXiv

cs.LG, stat.AP, stat.ML

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Explainability — 28× in this paper · explained in 3 episodes
• Accuracy — 20× in this paper · explained in 1 episode
• AUC — 15× in this paper · explained in 2 episodes
• Rank Graduation Fairness — 15× in this paper
• Normalized Fairness Contribution — 10× in this paper
• Benchmark — 7× in this paper · explained in 2 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Exchangeability — 5× in this paper · explained in 2 episodes
• Home Mortgage Disclosure Act — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• AUROC — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Shapley values — 1× in this paper · explained in 2 episodes

Sources

• Inherent Trade-Offs in the Fair Determination of Risk Scores
• Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn
• Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

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