Procedural Fairness in Machine Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Ziming, Huang, Changwu, Tang, Ke, Yao, Xin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2025)
by: Wang, Ziming, et al.
Published: (2025)
On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning
by: Yang, Zhi, et al.
Published: (2025)
by: Yang, Zhi, et al.
Published: (2025)
Integrating Knowledge Distillation Methods: A Sequential Multi-Stage Framework
by: Tian, Yinxi, et al.
Published: (2026)
by: Tian, Yinxi, et al.
Published: (2026)
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
by: Huang, Yinghui, et al.
Published: (2024)
by: Huang, Yinghui, et al.
Published: (2024)
Fairness-aware Multiobjective Evolutionary Learning
by: Zhang, Qingquan, et al.
Published: (2024)
by: Zhang, Qingquan, et al.
Published: (2024)
Procedural Fairness Through Decoupling Objectionable Data Generating Components
by: Tang, Zeyu, et al.
Published: (2023)
by: Tang, Zeyu, et al.
Published: (2023)
Beyond Procedure: Substantive Fairness in Conformal Prediction
by: Liu, Pengqi, et al.
Published: (2026)
by: Liu, Pengqi, et al.
Published: (2026)
PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning
by: Gao, Jiashi, et al.
Published: (2024)
by: Gao, Jiashi, et al.
Published: (2024)
Fairness in Graph Learning Augmented with Machine Learning: A Survey
by: Luo, Renqiang, et al.
Published: (2025)
by: Luo, Renqiang, et al.
Published: (2025)
Fairness in Machine Learning: A Survey
by: Caton, Simon, et al.
Published: (2020)
by: Caton, Simon, et al.
Published: (2020)
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study
by: Huang, Mingyu, et al.
Published: (2023)
by: Huang, Mingyu, et al.
Published: (2023)
What is Fair? Defining Fairness in Machine Learning for Health
by: Gao, Jianhui, et al.
Published: (2024)
by: Gao, Jianhui, et al.
Published: (2024)
Machine Learning Training Optimization using the Barycentric Correction Procedure
by: Ramos-Pulido, Sofia, et al.
Published: (2024)
by: Ramos-Pulido, Sofia, et al.
Published: (2024)
A Survey on Fairness for Machine Learning on Graphs
by: Laclau, Charlotte, et al.
Published: (2022)
by: Laclau, Charlotte, et al.
Published: (2022)
Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
by: Yang, Yi, et al.
Published: (2026)
by: Yang, Yi, et al.
Published: (2026)
Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
by: Wang, Angelina
Published: (2025)
by: Wang, Angelina
Published: (2025)
Measuring Fairness in Financial Transaction Machine Learning Models
by: Ayvaz, Deniz Sezin, et al.
Published: (2025)
by: Ayvaz, Deniz Sezin, et al.
Published: (2025)
Procedural Generation of Algorithm Discovery Tasks in Machine Learning
by: Goldie, Alexander D., et al.
Published: (2026)
by: Goldie, Alexander D., et al.
Published: (2026)
Distribution-Free Fair Federated Learning with Small Samples
by: Yin, Qichuan, et al.
Published: (2024)
by: Yin, Qichuan, et al.
Published: (2024)
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
by: Fawkes, Jake, et al.
Published: (2024)
by: Fawkes, Jake, et al.
Published: (2024)
Entropy-driven Fair and Effective Federated Learning
by: Wang, Lin, et al.
Published: (2023)
by: Wang, Lin, et al.
Published: (2023)
FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition
by: Debelak, Rudolf
Published: (2026)
by: Debelak, Rudolf
Published: (2026)
FairZK: A Scalable System to Prove Machine Learning Fairness in Zero-Knowledge
by: Zhang, Tianyu, et al.
Published: (2025)
by: Zhang, Tianyu, et al.
Published: (2025)
Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning
by: Huang, Zhuo, et al.
Published: (2026)
by: Huang, Zhuo, et al.
Published: (2026)
FairGC: Fairness-aware Graph Condensation
by: Gao, Yihan, et al.
Published: (2026)
by: Gao, Yihan, et al.
Published: (2026)
An Efficient Procedure for Computing Bayesian Network Structure Learning
by: Huang, Hongming, et al.
Published: (2024)
by: Huang, Hongming, et al.
Published: (2024)
Procedural Fairness via Group Counterfactual Explanation
by: Popoola, Gideon, et al.
Published: (2026)
by: Popoola, Gideon, et al.
Published: (2026)
Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics
by: Zhou, Quan
Published: (2024)
by: Zhou, Quan
Published: (2024)
Fair Graph Machine Learning under Adversarial Missingness Processes
by: Lina, Debolina Halder, et al.
Published: (2023)
by: Lina, Debolina Halder, et al.
Published: (2023)
RISE: Interactive Visual Diagnosis of Fairness in Machine Learning Models
by: Chen, Ray, et al.
Published: (2026)
by: Chen, Ray, et al.
Published: (2026)
Insights From Insurance for Fair Machine Learning
by: Fröhlich, Christian, et al.
Published: (2023)
by: Fröhlich, Christian, et al.
Published: (2023)
FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents
by: Dai, Yucong, et al.
Published: (2025)
by: Dai, Yucong, et al.
Published: (2025)
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
by: Liu, Mingxuan, et al.
Published: (2024)
by: Liu, Mingxuan, et al.
Published: (2024)
Fairness and Robustness in Machine Unlearning
by: Tran, Khoa, et al.
Published: (2025)
by: Tran, Khoa, et al.
Published: (2025)
Preserving AUC Fairness in Learning with Noisy Protected Groups
by: Wu, Mingyang, et al.
Published: (2025)
by: Wu, Mingyang, et al.
Published: (2025)
General Post-Processing Framework for Fairness Adjustment of Machine Learning Models
by: Eberhard, Léandre, et al.
Published: (2025)
by: Eberhard, Léandre, et al.
Published: (2025)
Ensemble Machine Learning and Statistical Procedures for Dynamic Predictions of Time-to-Event Outcomes
by: van Gerwen, Nina, et al.
Published: (2026)
by: van Gerwen, Nina, et al.
Published: (2026)
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
by: Souligne, Nick, et al.
Published: (2026)
by: Souligne, Nick, et al.
Published: (2026)
Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems
by: Amin, Mostafa M., et al.
Published: (2022)
by: Amin, Mostafa M., et al.
Published: (2022)
Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making
by: Bian, Zeyu, et al.
Published: (2026)
by: Bian, Zeyu, et al.
Published: (2026)
Similar Items
-
Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2025) -
On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning
by: Yang, Zhi, et al.
Published: (2025) -
Integrating Knowledge Distillation Methods: A Sequential Multi-Stage Framework
by: Tian, Yinxi, et al.
Published: (2026) -
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
by: Huang, Yinghui, et al.
Published: (2024) -
Fairness-aware Multiobjective Evolutionary Learning
by: Zhang, Qingquan, et al.
Published: (2024)