Fairness Regularization

Fairness regularization in machine learning aims to mitigate biases in algorithms that lead to unfair outcomes for different demographic groups. Current research focuses on integrating fairness constraints into various model architectures, including neural networks, survival analysis models, and graph neural networks, often employing techniques like distributionally robust optimization and contrastive regularization to achieve a balance between accuracy and fairness. This work is significant because it addresses ethical concerns surrounding algorithmic bias and seeks to improve the fairness and trustworthiness of machine learning systems across diverse applications. The development of flexible and efficient fairness regularization methods is a key area of ongoing investigation.

Papers

November 4, 2024

Fair In-Context Learning via Latent Concept Variables
Karuna Bhaila, Minh-Hao Van, Kennedy Edemacu, Chen Zhao, Feng Chen, Xintao Wu
Large Language Model Context Learning Latent Variable Fairness Regularization Latent Category

September 13, 2024

Fair CoVariance Neural Networks
Andrea Cavallo, Madeline Navarro, Santiago Segarra, Elvin Isufi
Neural Network Covariance Estimation Fairness Regularization Fair Principal Component Analysis

August 31, 2024

Fairness in Survival Analysis with Distributionally Robust Optimization
Shu Hu, George H. Chen
Procedural Fairness Distributionally Robust Optimization Fairness Regularization Survival Function

June 13, 2024

Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra
Model Fairness Graphical Model Unbiased Learning Fairness Regularization Graphical Lasso Precision Matrix

June 6, 2024

Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models
Jiahui Wu, Vanessa Frias-Martinez
Deep Learning Procedural Fairness Bias Correction Crime Prediction Biased Prediction Fairness Regularization

February 16, 2024

Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
Siamak Ghodsi, Seyed Amjad Seyedi, Eirini Ntoutsi
Group Fairness Fair Clustering Contrastive Regularization Fairness Regularization

January 29, 2024

Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters
Shahed Masoudian, Cornelia Volaucnik, Markus Schedl, Navid Rekabsaz
Language Model Classification Code App to App Retrieval Bias Mitigation Adapter Module Data Debiasing Fairness Regularization Bias Reduction

October 26, 2023

fairret: a Framework for Differentiable Fairness Regularization Terms
Maarten Buyl, MaryBeth Defrance, Tijl De Bie
New Framework Procedural Fairness Fairness Regularization Fairness Toolkits

September 22, 2023

Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments
Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen
Domain Generalization Domain Shift Environment Feature Model Generalization Fairness Regularization

September 20, 2023

Using Property Elicitation to Understand the Impacts of Fairness Regularizers
Jessie Finocchiaro
Loss Function Impact Assessment Algorithmic Decision Fairness Regularization Eliciting Knowledge Regularization Function

June 27, 2023

FAIRER: Fairness as Decision Rationale Alignment
Tianlin Li, Qing Guo, Aishan Liu, Mengnan Du, Zhiming Li, Yang Liu
Deep Neural Network Procedural Fairness Rationale Alignment Fairness Regularization Fairness Loss Fair Prediction Neuron Alignment

March 22, 2023

Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
LeArning Abstract Procedural Fairness Label Noise Long Tailed Data Fairness Regularization Performance Gap Class Balancing Technique

March 6, 2023

Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
Zhimeng Jiang, Xiaotian Han, Hongye Jin, Guanchu Wang, Rui Chen, Na Zou, Xia Hu
Distribution Shift Algorithmic Fairness Fairness Accuracy Fairness Regularization Fairness Method Weight Perturbation

March 1, 2023

Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization
Sangwon Jung, Taeeon Park, Sanghyuk Chun, Taesup Moon
Robust Optimization Group Fairness Fairness Metric Fairness Accuracy Fairness Regularization Fairness Aware Training Group Fairness Metric

October 24, 2022

Item-based Variational Auto-encoder for Fair Music Recommendation
Jinhyeok Park, Dain Kim, Dongwoo Kim
Real World Variational Auto Music Recommendation Fairness Regularization

July 7, 2022

Enhancing Fairness of Visual Attribute Predictors
Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger, Mengze Li, Erdem Ünal, Abouzar Eslami, Stefan Gumhold
Bias Mitigation Model Fairness Fairness Regularization Fairness Loss

June 26, 2022

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
Bang An, Zora Che, Mucong Ding, Furong Huang
Procedural Fairness Distribution Shift Group Fairness Algorithmic Fairness Model Fairness Fair Model Fairness Regularization

May 20, 2022

FairNorm: Fair and Fast Graph Neural Network Training
O. Deniz Kose, Yanning Shen
GNN Based Graph Based Learning Fairness Assessment Fairness Regularization Unified Normalization

May 18, 2022

Debiasing Neural Retrieval via In-batch Balancing Regularization
Yuantong Li, Xiaokai Wei, Zijian Wang, Shen Wang, Parminder Bhatia, Xiaofei Ma, Andrew Arnold
Neural Retrieval Fairness Regularization Significant Disparity Ranking Fairness

May 15, 2022

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt Schiele, Xing Xie
Semi Supervised Learning Fairness Regularization Adaptive Thresholding