Fairness Aware

Fairness-aware research focuses on mitigating bias and discrimination in machine learning models and human-robot interactions, aiming to ensure equitable outcomes across different demographic groups. Current research explores various fairness metrics and algorithms, including adaptations of Naive Bayes, Bayesian neural networks, and contrastive learning methods, often within the context of specific applications like recommendation systems, healthcare, and natural language processing. This work is crucial for building trustworthy and responsible AI systems, impacting both the development of fairer algorithms and the ethical deployment of AI in sensitive domains.

Papers

August 28, 2023

Fair Few-shot Learning with Auxiliary Sets
Song Wang, Jing Ma, Lu Cheng, Jundong Li
Shot Learning Fairness Aware Fairness Perception Fairness Performance Fairness Optimization Auxiliary Item Relationship

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

March 16, 2023

Fairness-aware Differentially Private Collaborative Filtering
Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying
Differential Privacy Algorithmic Fairness Fairness Aware Differential Privacy Mechanism User Oriented Fairness

February 19, 2023

SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Tianci Liu, Haoyu Wang, Yaqing Wang, Xiaoqian Wang, Lu Su, Jing Gao
Unified Framework Multi Label Multi Label Classification Fairness Aware Label Scenario

February 3, 2023

Group Fairness in Non-monotone Submodular Maximization
Jing Yuan, Shaojie Tang
Submodular Maximization Group Fairness Fairness Aware Monotone Submodular Function Group Fairness Constraint

January 15, 2023

Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
Songkai Xue, Yuekai Sun, Mikhail Yurochkin
Stochastic Optimization Fairness Aware

October 18, 2022

Towards Fair Classification against Poisoning Attacks
Han Xu, Xiaorui Liu, Yuxuan Wan, Jiliang Tang
Poisoning Attack Fair Classification Fairness Aware Protected Attribute Fairness Performance Trade Malicious Training

June 19, 2022

Fairness-aware Model-agnostic Positive and Unlabeled Learning
Ziwei Wu, Jingrui He
Positive Unlabeled Learning Fair Classification Fairness Aware Machine Fairness Aware Unlabeled Learning Agnostic Model Optimal Fair

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 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

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 23, 2022

Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features
Stelios Boulitsakis-Logothetis
Raw Data Fairness Aware Machine Fairness Aware Naive Bayes Proportional Fairness Fairness Criterion Protected Attribute