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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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
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Efficient Active Auditing of Multi-Group Fairness with Bias Probes
arXiv:2609.40034v1 Announce Type: cross Abstract: Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM)...
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.
arXiv AI
Sep 16
FairLint-DL: An IDE-Native Tool for Fairness Debugging of Deep Learning Software
arXiv:2609.16321v1 Announce Type: cross Abstract: Existing fairness analysis tools predominantly operate as post-training evaluation frameworks, requiring practitioners to complete the full model dev...
arXiv Machine Learning
Sep 10
The Role of Uncertainty in Assessing the Fairness of Machine Learning Models
arXiv:2609.07959v1 Announce Type: cross Abstract: Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their...
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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.