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

July 11, 2023

Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
James Atwood, Tina Tian, Ben Packer, Meghana Deodhar, Jilin Chen, Alex Beutel, Flavien Prost, Ahmad Beirami
Multi Label Group Fairness Interleaving Method Machine Learning Fairness Scalable Solution

July 10, 2023

Fairness-aware Federated Minimax Optimization with Convergence Guarantee
Gerry Windiarto Mohamad Dunda, Shenghui Song
Procedural Fairness Group Fairness Convergence Guarantee Fairness Constraint Lagrangian Relaxation Federated Minimax Optimization

July 9, 2023

On The Impact of Machine Learning Randomness on Group Fairness
Prakhar Ganesh, Hongyan Chang, Martin Strobel, Reza Shokri
Global Impact Group Fairness Model Fairness Inherent Randomness

July 2, 2023

Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems
Furkan Gursoy, Ioannis A. Kakadiaris
Procedural Fairness Group Fairness Fairness Metric Automated Decision Making Group Disparity

June 21, 2023

Sampling Individually-Fair Rankings that are Always Group Fair
Sruthi Gorantla, Anay Mehrotra, Amit Deshpande, Anand Louis
Group Fairness Algorithmic Fairness Group Setting Fair Ranking Group Fairness Constraint

June 15, 2023

May 31, 2023

Doubly Constrained Fair Clustering
John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang
Group Fairness Fairness Notion Fair Clustering

May 30, 2023

Adapting Fairness Interventions to Missing Values
Raymond Feng, Flavio P. Calmon, Hao Wang
Group Fairness Algorithmic Fairness Policy Value Fair Classification Fairness Intervention

May 22, 2023

Fairness of ChatGPT
Yunqi Li, Lanjing Zhang, Yongfeng Zhang
ChatGPT Generated Conversation Procedural Fairness Bias Mitigation Group Fairness Fairness Evaluation Fairness Performance

May 17, 2023

Mitigating Group Bias in Federated Learning: Beyond Local Fairness
Ganghua Wang, Ali Payani, Myungjin Lee, Ramana Kompella
Group Fairness Model Fairness Mitigating Bias Fairness Aware Training Local Fairness Fair Training

May 11, 2023

A statistical approach to detect sensitive features in a group fairness setting
Guilherme Dean Pelegrina, Miguel Couceiro, Leonardo Tomazeli Duarte
Group Fairness Sensitive Attribute Biased Decision Independence Testing Statistical Approach

May 5, 2023

Statistical Inference for Fairness Auditing
John J. Cherian, Emmanuel J. Candès
Black Box Model Group Fairness Algorithmic Fairness Statistical Inference Predictive Inference Fairness Audit

April 27, 2023

Fairness Uncertainty Quantification: How certain are you that the model is fair?
Abhishek Roy, Prasant Mohapatra
Full Model Stochastic Gradient Descent Group Fairness Fairness Aware Machine Uncertainty Aware Fairness

April 19, 2023

Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Edward A. Small, Kacper Sokol, Daniel Manning, Flora D. Salim, Jeffrey Chan
Group Fairness Post Processing Group Setting Relative Probability Individual Fairness Equalized Odds

April 1, 2023

Fair-CDA: Continuous and Directional Augmentation for Group Fairness
Rui Sun, Fengwei Zhou, Zhenhua Dong, Chuanlong Xie, Lanqing Hong, Jiawei Li, Rui Zhang, Zhen Li, Zhenguo Li
Fine Grained Group Fairness Fairness Constraint Continuous Speech

March 26, 2023

FairGAT: Fairness-aware Graph Attention Networks
O. Deniz Kose, Yanning Shen
Graph Neural Network Graph Attention Network Group Fairness Algorithmic Bias Fairness Assessment

March 15, 2023

DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Sungwon Han, Seungeon Lee, Fangzhao Wu, Sundong Kim, Chuhan Wu, Xiting Wang, Xing Xie, Meeyoung Cha
Self Supervised Group Fairness Group Setting Counterfactual Fairness Multiple Level Fair Representation Learning Fairness Criterion Contrastive Self Supervision

March 1, 2023