Balancing Fairness

Balancing fairness in machine learning and related fields aims to mitigate biases and discriminatory outcomes in algorithms while maintaining accuracy and efficiency. Current research focuses on developing methods that achieve fairness through data transformations (e.g., using normalizing flows), adaptive model architectures (e.g., FairViT), and algorithmic modifications (e.g., fairness-aware boosting), often addressing the inherent trade-off between fairness and accuracy. This work is crucial for ensuring equitable outcomes in applications ranging from loan applications and hiring processes to resource allocation and environmental sustainability, impacting both the ethical development of AI and its societal impact.

Papers

September 23, 2024

AdapFair: Ensuring Continuous Fairness for Machine Learning Operations
Yinghui Huang, Zihao Tang, Xiangyu Chang
Machine Learning Debiasing Method Data Transformation Downstream Classification Balancing Fairness Fairness Guarantee

July 20, 2024

FairViT: Fair Vision Transformer via Adaptive Masking
Bowei Tian, Ruijie Du, Yanning Shen
Vision Transformer Vision Task Balancing Fairness Adaptive Mask

June 24, 2024

Cherry on the Cake: Fairness is NOT an Optimization Problem
Marco Favier, Toon Calders
Procedural Fairness Optimization Problem Fairness Constraint Fair Decision Balancing Fairness Multi Layered Cake Cake Cutting

June 13, 2024

FADE: Towards Fairness-aware Augmentation for Domain Generalization via Classifier-Guided Score-based Diffusion Models
Yujie Lin, Dong Li, Chen Zhao, Minglai Shao
Fair Machine Learning Score Based Diffusion Model Fairness Method Balancing Fairness Fair Synthetic

March 22, 2024

Balancing Fairness and Efficiency in Energy Resource Allocations
Jiayi Li, Matthew Motoki, Baosen Zhang
High Efficiency Resource Allocation Balancing Fairness Fairness Performance Trade Efficient Allocation Fair Resource Allocation

March 12, 2024

Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Zachary McBride Lazri, Danial Dervovic, Antigoni Polychroniadou, Ivan Brugere, Dana Dachman-Soled, Min Wu
Machine Learning Binary Classification Fairness Notion Fair Decision Bayes Classifier Balancing Fairness

September 10, 2023

Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF
Amanda Aird, Cassidy All, Paresha Farastu, Elena Stefancova, Joshua Sun, Nicholas Mattei, Robin Burke
Fairness Evaluation Social Choice Balancing Fairness Recommendation Fairness Fairness Aware Recommendation

July 20, 2023

Fairness-Aware Client Selection for Federated Learning
Yuxin Shi, Zelei Liu, Zhuan Shi, Han Yu
Balancing Fairness Reputation System

June 1, 2023

Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning
Peizhong Ju, Arnob Ghosh, Ness B. Shroff
Reinforcement Learning Markov Decision Process Multi Agent Policy Gradient Achieving Fairness Balancing Fairness

April 12, 2023

Maximal Fairness
MaryBeth Defrance, Tijl De Bie
Fairness Notion Fairness Measure Balancing Fairness

December 13, 2022

Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre Dognin, Kush R. Varshney
Machine Learning Model Procedural Fairness External Influence Inherent Bias Biased Training Balancing Fairness Pre Trained Classifier

November 8, 2022

Bridging Fairness and Environmental Sustainability in Natural Language Processing
Marius Hessenthaler, Emma Strubell, Dirk Hovy, Anne Lauscher
Natural Language Processing Model Fairness Stereotypical Bias Balancing Fairness Environment Optimization FAIRness Approach

July 21, 2022

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Wenda Chu, Chulin Xie, Boxin Wang, Linyi Li, Lang Yin, Arash Nourian, Han Zhao, Bo Li
Procedural Fairness Heterogeneous Data Heterogeneous Agent Balancing Fairness Agent Training Fair Federated Learning

June 23, 2022

Context matters for fairness -- a case study on the effect of spatial distribution shifts
Siamak Ghodsi, Harith Alani, Eirini Ntoutsi
Case Study Procedural Fairness Mixed Effect Context Information Distribution Shift Temporal Shift Fairness Intervention Balancing Fairness Fairness Aware Learning

April 22, 2022

Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models
Abdulaziz A. Almuzaini, Vivek K. Singh
Sentiment Analysis Black Box Model Balancing Fairness

March 3, 2022

Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models
Zhibo Wang, Xiaowei Dong, Henry Xue, Zhifei Zhang, Weifeng Chiu, Tao Wei, Kui Ren
Bias Mitigation Fairness Aware Achieving Fairness Balancing Fairness

February 7, 2022

SLIDE: a surrogate fairness constraint to ensure fairness consistency
Kunwoong Kim, Ilsang Ohn, Sara Kim, Yongdai Kim
Fairness Constraint Skill Learning AI Fairness Balancing Fairness

January 4, 2022

Parity-based Cumulative Fairness-aware Boosting
Vasileios Iosifidis, Arjun Roy, Eirini Ntoutsi
Class Imbalance Fairness Aware Machine Balancing Fairness

December 17, 2021

Balancing Fairness and Robustness via Partial Invariance
Moulik Choraria, Ibtihal Ferwana, Ankur Mani, Lav R. Varshney
Native Robustness Invariant Feature Invariant Risk Minimization Balancing Fairness