Individual Fairness

Individual fairness in machine learning aims to ensure that similar individuals are treated similarly by algorithms, regardless of protected attributes like race or gender, addressing a critical ethical concern in AI. Current research focuses on developing methods to certify and quantify individual fairness within various model architectures, including deep neural networks and graph neural networks, often employing techniques like reweighting, adversarial training, and metric learning to achieve this goal. This work is significant because it strives to create more equitable and trustworthy AI systems, impacting both the theoretical understanding of fairness and the practical deployment of AI in sensitive applications.

Papers

September 6, 2024

AGR: Age Group fairness Reward for Bias Mitigation in LLMs
Shuirong Cao, Ruoxi Cheng, Zhiqiang Wang
Medical LLM Bias Mitigation Individual Fairness Demographic Bias Fair Incentive

September 5, 2024

FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks
Brian Hyeongseok Kim, Jingbo Wang, Chao Wang
Deep Neural Network Individual Fairness Fairness Property Fairness Datasets

June 24, 2024

Efficient k-means with Individual Fairness via Exponential Tilting
Shengkun Zhu, Jinshan Zeng, Yuan Sun, Sheng Wang, Xiaodong Li, Zhiyong Peng
Resource Allocation Fairness Metric Tilting Angle Individual Fairness Fair Clustering

June 10, 2024

Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition
Andreas Triantafyllopoulos, Björn Schuller
Speech Emotion Recognition Underlying Emotion Individual Fairness Novel Evaluation Enrollment Utterance

June 9, 2024

Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback
Emilia Agis Lerner, Florian E. Dorner, Elliott Ash, Naman Goel
Procedural Fairness Human Feedback Preference Feedback Content Moderation Qualitative Difference Individual Fairness Fairness Preference

June 5, 2024

Reproducibility study of FairAC
Gijs de Jong, Macha J. Meijer, Derck W. E. Prinzhorn, Harold Ruiter
Group Fairness Individual Fairness Reproducibility Study Attribute Completion

May 2, 2024

Individual Fairness Through Reweighting and Tuning
Abdoul Jalil Djiberou Mahamadou, Lea Goetz, Russ Altman
Model Fairness Dynamic Reweighting Graph Laplacian Hyper Tune Inherent Bias Individual Fairness

April 26, 2024

Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
Duna Zhan, Dongliang Guo, Pengsheng Ji, Sheng Li
Graph Neural Network Graph Learning Group Fairness Group Setting Individual Fairness

March 11, 2024

Monotone Individual Fairness
Yahav Bechavod
Individual Fairness Online Classification Fairness Property Fairness Problem Oracle Efficient Algorithm

February 20, 2024

GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
Anuj Kumar Sirohi, Anjali Gupta, Sayan Ranu, Sandeep Kumar, Amitabha Bagchi
Graph Neural Network Group Fairness Fairness Constraint Individual Fairness Individual Perspective Group Fairness Constraint

February 8, 2024

Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits
Subham Pokhriyal, Shweta Jain, Ganesh Ghalme, Swapnil Dhamal, Sujit Gujar
Stochastic Bandit Individual Fairness Stochastic Multi Armed Bandit Fairness Performance Exposure Fairness

February 5, 2024

On the Impact of Output Perturbation on Fairness in Binary Linear Classification
Vitalii Emelianov, Michaël Perrot
Differential Privacy Global Impact Procedural Fairness Group Fairness Linear Classifier Privacy Preserving Machine Learning Individual Fairness

January 13, 2024

On the (In)Compatibility between Group Fairness and Individual Fairness
Shizhou Xu, Thomas Strohmer
Group Fairness Individual Fairness Pareto Optimality Fairness Guarantee Statistical Parity Parity Bit

November 20, 2023

Certification of Distributional Individual Fairness
Matthew Wicker, Vihari Piratia, Adrian Weller
Algorithmic Fairness Individual Fairness Local Fairness Quasi Convex Optimization

October 30, 2023

Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
Adversarial Robustness Adversarial Perturbation Causal Structure Structural Causal Model Individual Fairness Causal Fairness

September 11, 2023

Re-formalization of Individual Fairness
Toshihiro Kamishima
Fairness Notion Individual Fairness Fairness Aware Machine Equalized Odds Fair Prediction

August 17, 2023

Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
Ahmad-Reza Ehyaei, Kiarash Mohammadi, Amir-Hossein Karimi, Samira Samadi, Golnoosh Farnadi
Native Robustness Adversarial Robustness Adversarial Perturbation Structural Causal Model Sensitive Attribute Individual Fairness Heterogeneous Datasets

May 25, 2023

GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
Paiheng Xu, Yuhang Zhou, Bang An, Wei Ai, Furong Huang
Graph Neural Network Algorithmic Fairness GNN Model Fair Representation Individual Fairness Fairness Assessment

April 21, 2023

Individual Fairness in Bayesian Neural Networks
Alice Doherty, Matthew Wicker, Luca Laurenti, Andrea Patane
Bayesian Neural Network Individual Fairness Fairness Aware Fairness Benchmark

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