:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zanna, Khadija, Sano, Akane
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computers and Society
Online Access:	https://arxiv.org/abs/2404.08230
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring
by: Zanna, Khadija, et al.
Published: (2025)

Uncovering Bias Paths with LLM-guided Causal Discovery: An Active Learning and Dynamic Scoring Approach
by: Zanna, Khadija, et al.
Published: (2025)

Rethinking Fair Representation Learning for Performance-Sensitive Tasks
by: Jones, Charles, et al.
Published: (2024)

FairMT: Fairness for Heterogeneous Multi-Task Learning
by: Hu, Guanyu, et al.
Published: (2025)

Fairness in Multi-Task Learning via Wasserstein Barycenters
by: Hu, François, et al.
Published: (2023)

Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
by: Yang, Zeyu, et al.
Published: (2024)

Transferring Fairness using Multi-Task Learning with Limited Demographic Information
by: Aguirre, Carlos, et al.
Published: (2023)

Machine Learning Predicts Upper Secondary Education Dropout as Early as the End of Primary School
by: Psyridou, Maria, et al.
Published: (2024)

What is Fair? Defining Fairness in Machine Learning for Health
by: Gao, Jianhui, et al.
Published: (2024)

Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems
by: Wilms, Mieke, et al.
Published: (2026)

AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
by: Huang, Yinghui, et al.
Published: (2024)

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
by: Fawkes, Jake, et al.
Published: (2024)

Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2025)

Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning
by: Raumanns, Ralf, et al.
Published: (2024)

Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
by: Kim, Woojin, et al.
Published: (2025)

The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models
by: Anderson, Joshua Wolff, et al.
Published: (2025)

Insights From Insurance for Fair Machine Learning
by: Fröhlich, Christian, et al.
Published: (2023)

Learning Pareto-Optimal Pandemic Intervention Policies with MORL
by: Chen, Marian, et al.
Published: (2025)

Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems
by: Amin, Mostafa M., et al.
Published: (2022)

Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
by: Somerstep, Seamus, et al.
Published: (2024)

Evaluating Fair Feature Selection in Machine Learning for Healthcare
by: Zawad, Md Rahat Shahriar, et al.
Published: (2024)

Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
by: Wang, Angelina
Published: (2025)

Fair Machine Learning in Healthcare: A Review
by: Feng, Qizhang, et al.
Published: (2022)

FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning
by: Roy, Arjun, et al.
Published: (2023)

Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making
by: Chohlas-Wood, Alex, et al.
Published: (2021)

Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling
by: Wang, Xiaoyang, et al.
Published: (2025)

FAIRPLAI: A Human-in-the-Loop Approach to Fair and Private Machine Learning
by: Sanchez Jr., David, et al.
Published: (2025)

Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction
by: Chen, Yawen, et al.
Published: (2025)

Learning Fair Models without Sensitive Attributes: A Generative Approach
by: Zhu, Huaisheng, et al.
Published: (2022)

Tuning Derivatives for Causal Fairness in Machine Learning
by: Edström, Filip, et al.
Published: (2026)

On The Fairness Impacts of Hardware Selection in Machine Learning
by: Nelaturu, Sree Harsha, et al.
Published: (2023)

FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport
by: Liu, Pengxi, et al.
Published: (2025)

An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems
by: Thu, Huyen Giang Thi, et al.
Published: (2024)

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
by: Bothmann, Ludwig, et al.
Published: (2023)

Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems
by: Yan, Jing Nathan, et al.
Published: (2025)

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
by: Liu, Mingxuan, et al.
Published: (2024)

Data vs. Model Machine Learning Fairness Testing: An Empirical Study
by: Shome, Arumoy, et al.
Published: (2024)

Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review
by: Benítez-Peña, Sandra, et al.
Published: (2026)

Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits
by: Leshem, Amir
Published: (2023)

Balancing Fairness and Performance in Healthcare AI: A Gradient Reconciliation Approach
by: Wang, Xiaoyang, et al.
Published: (2025)