Group Fairness

Group fairness in machine learning aims to ensure that algorithms produce equitable outcomes across different demographic groups, mitigating biases that might disproportionately harm certain populations. Current research focuses on developing and evaluating methods to achieve fairness across various model architectures and learning paradigms, including federated learning, graph neural networks, and large language models, often employing techniques like post-processing, re-weighting, and adversarial training to address issues like spurious correlations and intersectional bias. This field is crucial for building trustworthy and ethical AI systems, impacting applications ranging from healthcare and criminal justice to hiring and loan applications by promoting fairness and reducing discrimination.

Papers

February 22, 2023

Uncovering Bias in Face Generation Models
Cristian Muñoz, Sara Zannone, Umar Mohammed, Adriano Koshiyama
GAN Model Bias Mitigation Group Fairness Face Generation Feature Perturbation Bias Discovery

February 16, 2023

Group Fairness with Uncertainty in Sensitive Attributes
Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell
Procedural Fairness High Uncertainty Anticipation Group Fairness Fairness Constraint Sensitive Attribute Fair Predictor Group Fairness Constraint

February 14, 2023

When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness
Natasa Krco, Thibault Laugel, Vincent Grari, Jean-Michel Loubes, Marcin Detyniecki
Absolute Stance Bias Bias Mitigation Group Fairness Fair Machine Learning Model Multiplicity Algorithmic Arbitrariness

February 11, 2023

Fairness-aware Multi-view Clustering
Lecheng Zheng, Yada Zhu, Jingrui He
Data Heterogeneity Group Fairness Heterogeneous Data Multi View Clustering Fair Clustering

February 5, 2023

Improving Fair Training under Correlation Shifts
Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh
Group Fairness Model Fairness Fair Algorithm Fairness Mitigation Correlation Shift Fair Training

February 3, 2023

Group Fairness in Non-monotone Submodular Maximization
Jing Yuan, Shaojie Tang
Submodular Maximization Group Fairness Fairness Aware Monotone Submodular Function Group Fairness Constraint

January 31, 2023

January 25, 2023

Increasing Fairness via Combination with Learning Guarantees
Yijun Bian, Kun Zhang, Anqi Qiu, Nanguang Chen
Procedural Fairness Group Fairness New Combination Fairness Measure Learning Guarantee

January 21, 2023

From Pseudorandomness to Multi-Group Fairness and Back
Cynthia Dwork, Daniel Lee, Huijia Lin, Pranay Tankala
Group Fairness Random Number Generator Predictive Algorithm Outcome Indistinguishability Regular Graph

January 20, 2023

Within-group fairness: A guidance for more sound between-group fairness
Sara Kim, Kyusang Yu, Yongdai Kim
Procedural Fairness Artificial Intelligence Model Group Fairness Human Guidance

December 29, 2022

November 28, 2022

FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee
Puheng Li, James Zou, Linjun Zhang
Group Fairness Fair Classification FAir Classifier Distribution Free Group Fairness Constraint

November 27, 2022

"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations
André Artelt, Barbara Hammer
Line by Line Explanation Counterfactual Explanation Complexity Matter Movie Recommendation Group Fairness Model Agnostic

November 18, 2022

On the Alignment of Group Fairness with Attribute Privacy
Jan Aalmoes, Vasisht Duddu, Antoine Boutet
Alignment Problem Group Fairness Inference Attack Adversarial Debiasing Attribute Privacy

November 11, 2022

Identifying, measuring, and mitigating individual unfairness for supervised learning models and application to credit risk models
Rasoul Shahsavarifar, Jithu Chandran, Mario Inchiosa, Amit Deshpande, Mario Schlener, Vishal Gossain, Yara Elias, Vinaya Murali
Application Proficiency Supervised Learning Group Fairness Individual Fairness Fairness Property Fairness Mitigation Credit Risk Model

October 29, 2022

Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework
Wei-Wei Du, Wei-Yao Wang, Wen-Chih Peng
Recommendation System Group Fairness Recommendation Quality Recommendation Fairness

October 28, 2022

Differential Privacy has Bounded Impact on Fairness in Classification
Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi
Classification Code Differential Privacy Procedural Fairness Group Fairness Private Model Lipschitz Operator

October 19, 2022

Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation
Joachim Baumann, Christoph Heitz
Practical Implementation Group Fairness Equitable Healthcare System Prediction Based Decision Making Moral Evaluation