Fairness Aware Learning

Fairness-aware learning aims to mitigate biases in machine learning models, ensuring equitable outcomes across different demographic groups. Current research focuses on developing algorithms and model architectures that incorporate fairness constraints during training, addressing challenges in various settings such as incremental learning, federated learning, and multimodal data fusion. These advancements are crucial for building trustworthy AI systems, improving the reliability of predictions across diverse populations, and promoting fairness in critical applications like recruitment and education.

Papers

October 2, 2024

Fair Class-Incremental Learning using Sample Weighting
Jaeyoung Park, Minsu Kim, Steven Euijong Whang
Class Incremental Learning Model Fairness Fairness Accuracy Sample Weight Fairness Aware Learning

June 17, 2024

Exploring Fusion Techniques in Multimodal AI-Based Recruitment: Insights from FairCVdb
Swati Swati, Arjun Roy, Eirini Ntoutsi
DCU Insight AQ Multimodal Data Multimodal Phenomenon Fairness Constraint Fusion Method Fairness Aware Learning

April 22, 2024

Fair Concurrent Training of Multiple Models in Federated Learning
Marie Siew, Haoran Zhang, Jong-Ik Park, Yuezhou Liu, Yichen Ruan, Lili Su, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong
Federated Learning Multiple Model Fairness Aware Learning Fair Training

January 27, 2024

SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
Zhihao Wang, Yiqun Xie, Zhili Li, Xiaowei Jia, Zhe Jiang, Aolin Jia, Shuo Xu
Physical Simulation Mechanistic Model Fairness Aware Simulation Model Fairness Aware Learning Fairness Verification

July 21, 2023

On the Vulnerability of Fairness Constrained Learning to Malicious Noise
Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin Stangl
Fairness Constraint Fairness Notion Latent Vulnerability Malicious User Fairness Aware Learning

January 9, 2023

A review of clustering models in educational data science towards fairness-aware learning
Tai Le Quy, Gunnar Friege, Eirini Ntoutsi
Narrative Review Fair Clustering Educational Data Fairness Aware Learning

December 26, 2022

Bias Mitigation Framework for Intersectional Subgroups in Neural Networks
Narine Kokhlikyan, Bilal Alsallakh, Fulton Wang, Vivek Miglani, Oliver Aobo Yang, David Adkins
Neural Network Bias Mitigation Fairness Constraint Fairness Aware Learning

September 12, 2022

Fairness in Forecasting of Observations of Linear Dynamical Systems
Quan Zhou, Jakub Marecek, Robert N. Shorten
Procedural Fairness State of the Art Forecasting Evolving Observation Linear Dynamical System Fairness Aware Learning Subgroup Fairness

September 2, 2022

Exploiting Fairness to Enhance Sensitive Attributes Reconstruction
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
Procedural Fairness Fairness Constraint Fairness Aware Machine Protected Attribute Fairness Aware Learning

August 22, 2022

Evaluation of group fairness measures in student performance prediction problems
Tai Le Quy, Thi Huyen Nguyen, Gunnar Friege, Eirini Ntoutsi
Global Evaluation Group Fairness Educational Data Student Performance Prediction Fairness Aware 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

June 16, 2022

Learning to Teach Fairness-aware Deep Multi-task Learning
Arjun Roy, Eirini Ntoutsi
LeArning Abstract Multi Task Learning Fairness Accuracy Fairness Aware Learning Pf Agp

May 20, 2022

Survey on Fair Reinforcement Learning: Theory and Practice
Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher, Mykola Pechenizkiy
Reinforcement Learning Timely Survey Theoretical Understanding Practice Mode Group Fairness Fairness Aware Fairness Aware Learning FAIRness Approach

November 29, 2021

Learning Fair Classifiers with Partially Annotated Group Labels
Sangwon Jung, Sanghyuk Chun, Taesup Moon
Group Fairness Group Annotation Fairness Aware Learning Fair Classifier

November 21, 2021

End-to-end Learning for Fair Ranking Systems
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu
Learning to Rank Fair Ranking Fairness Aware Learning Fair Learning to Rank