arXiv Machine Learning
Jun 2
FedCF: Fair Federated Conformal Prediction
arXiv:2509. 22907v2 Announce Type: replace Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models.
Beyond Procedure: Substantive Fairness in Conformal Prediction
arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.
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arXiv Machine Learning
Aug 10
Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs
arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.
arXiv AI
Jun 2
Demystifying the Optimal Fair Classifier in Multi-Class Classification
arXiv:2606. 00656v1 Announce Type: cross Abstract: Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models.
arXiv Machine Learning
Jun 19
Data Bias Mitigation under Coverage Constraints & The Price of Fairness
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
arXiv AI
Jun 17
No-Free-Fairness: Fundamental Limits and Trade-offs in Learning Systems
arXiv:2606. 17810v1 Announce Type: cross Abstract: In this paper, we establish a set of theoretical impossibility results, termed the No-Free-Fairness theorems, that identify three fundamental sources of disparity in learning systems.
arXiv Machine Learning
Jul 14
Fairness Constraints in High-Dimensional Generalized Linear Models
arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.
arXiv Machine Learning
Jul 13
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness
arXiv:2607. 08953v1 Announce Type: new Abstract: Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes.
Hugging Face Trending Papers
Jul 9
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness
Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidance for selecting fairness strategies, where disparities may arise across intersectional subgroups and across multiple stages of the modeling lifecycle.
arXiv AI
Jul 13
Tuning Derivatives for Causal Fairness in Machine Learning
arXiv:2605. 05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age.
arXiv Machine Learning
Aug 3
End-to-End Fairness Optimization with Fair Decision-Focused Learning
arXiv:2607. 29441v1 Announce Type: new Abstract: Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages.
arXiv AI
2d ago
Training Fair Tabular Foundation Models
arXiv:2608. 14211v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.
arXiv AI
Jun 26
Statistical and Structural Approaches to Algorithmic Fairness
arXiv:2606. 26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity.