Model Fairness

Model fairness addresses the ethical concern of algorithmic bias in machine learning, aiming to ensure that models perform equally well across different demographic groups. Current research focuses on developing and comparing methods to mitigate bias during various stages of model development, including data preprocessing, model training (e.g., using techniques like contrastive learning, regularization, and multi-task learning), and post-processing, often employing graph neural networks and large language models. This research is crucial for building trustworthy AI systems, impacting fields like healthcare, criminal justice, and education by promoting equitable outcomes and reducing discriminatory practices.

Papers

May 1, 2023

Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity
Josh Gardner, Renzhe Yu, Quan Nguyen, Christopher Brooks, Rene Kizilcec
Procedural Fairness Model Performance Future Implication Model Fairness Zero Shot Transfer Intersectional Fairness Teaching Model

March 25, 2023

Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics
Leonardo Iurada, Silvia Bucci, Timothy M. Hospedales, Tatiana Tommasi
Full Model Procedural Fairness Cross Domain Metric Library New Perspective Model Fairness Fairness Method Visual Bias

March 22, 2023

Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
Yanbiao Ma, Licheng Jiao, Fang Liu, Maoji Wen, Lingling Li, Wenping Ma, Shuyuan Yang, Xu Liu, Puhua Chen
Human Prediction Procedural Fairness DNN Framework Model Bias Model Fairness Gaussian Curvature

March 13, 2023

Addressing Biases in the Texts using an End-to-End Pipeline Approach
Shaina Raza, Syed Raza Bashir, Sneha, Urooj Qamar
Procedural Fairness Text Based Fair Machine Learning Social Medium Data Model Fairness Medium Bias Addressing Bias End to End Pipeline

March 8, 2023

Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition
Varsha Suresh, Desmond C. Ong
Absolute Stance Bias Contrastive Loss Bias Mitigation Facial Expression Recognition Model Fairness Facial Action Unit Correlation Bias

March 6, 2023

Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis
Raghav Mehta, Changjian Shui, Tal Arbel
Deep Learning Procedural Fairness Medical Image Analysis Brain Tumor Segmentation Model Fairness Medical Image Analysis Task Deep Uncertainty

March 1, 2023

Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
Zahra Ashktorab, Benjamin Hoover, Mayank Agarwal, Casey Dugan, Werner Geyer, Hao Bang Yang, Mikhail Yurochkin
Text Classification Group Fairness Model Fairness Algorithmic Bias Individual Fairness Fairness Evaluation Individual Perspective Fairness Assessment

February 16, 2023

Individual Fairness under Uncertainty
Wenbin Zhang, Zichong Wang, Juyong Kim, Cheng Cheng, Thomas Oommen, Pradeep Ravikumar, Jeremy Weiss
High Uncertainty Anticipation Model Fairness Algorithmic Bias Individual Fairness Fairness Research

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

November 23, 2022

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Josh Gardner, Zoran Popović, Ludwig Schmidt
Baseline Result Tree Specie Model Fairness Group Robustness

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

October 31, 2022

Improving Fairness in Image Classification via Sketching
Ruichen Yao, Ziteng Cui, Xiaoxiao Li, Lin Gu
Deep Neural Network Image Classification Model Fairness Fairness Improvement Sketch to Image Source Bias Bias Neuron

September 17, 2022

Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances
Minghua Ma, Zhao Tian, Max Hort, Federica Sarro, Hongyu Zhang, Qingwei Lin, Dongmei Zhang
Model Fairness Seed Area Artificial Intelligence Bias Instance Discrimination FAIrness Testing

July 20, 2022

Mitigating Algorithmic Bias with Limited Annotations
Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu
Bias Mitigation Model Fairness Algorithmic Bias Limited Annotation

July 13, 2022

Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning
Damien Dablain, Bartosz Krawczyk, Nitesh Chawla
Machine Learning Absolute Stance Bias Class Imbalance Group Fairness Algorithmic Fairness Model Fairness Imbalanced Learning Holistic Perception

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 29, 2022

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen, Sanghamitra Dutta, Alan Mishler, Siddharth Garg
Neural Network Procedural Fairness Complex Process Model Fairness Efficient Optimization Cautionary TALE Parameterized Regime Fairness Aware Training

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

June 7, 2022

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Zhongliang Yang, Yongfeng Huang, Xing Xie
Fair Machine Learning Model Fairness Fair Representation Fairness Aware FAIR Federated Learning Adversarial Contrastive

May 31, 2022

To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier
Camille Olivia Little, Michael Weylandt, Genevera I Allen
Model Fairness Fairness Accuracy Pareto Frontier

Model Fairness

Papers

Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity

Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics

Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds

Addressing Biases in the Texts using an End-to-End Pipeline Approach

Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition

Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis

Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness

Individual Fairness under Uncertainty

Improving Fair Training under Correlation Shifts

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation

Bridging Fairness and Environmental Sustainability in Natural Language Processing

Improving Fairness in Image Classification via Sketching

Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances

Mitigating Algorithmic Bias with Limited Annotations

Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning

Enhancing Fairness of Visual Attribute Predictors

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier