Fair Machine Learning

Fair machine learning aims to develop algorithms that make unbiased predictions, avoiding discrimination against sensitive attributes like race or gender. Current research focuses on mitigating bias through various techniques, including modifying model architectures (e.g., using mixed-effects models or incorporating fairness penalties into neural networks), developing fairness-aware data augmentation methods, and employing active learning strategies to improve data representation. This field is crucial for ensuring equitable outcomes in applications ranging from healthcare and loan applications to criminal justice, promoting responsible and ethical use of AI.

Papers

January 20, 2024

Long-Term Fair Decision Making through Deep Generative Models
Yaowei Hu, Yongkai Wu, Lu Zhang
Causal Graph Fair Machine Learning Long Term Fairness

November 21, 2023

Fair Polylog-Approximate Low-Cost Hierarchical Clustering
Marina Knittel, Max Springer, John Dickerson, MohammadTaghi Hajiaghayi
Fair Machine Learning Hierarchical Clustering Fair Clustering Logarithmic Barrier

November 10, 2023

Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
Jinwon Sohn, Qifan Song, Guang Lin
Fair Machine Learning Sensitive Attribute Fairness Aware Machine Random Sampling Fairness Aware Training

November 7, 2023

Loss Balancing for Fair Supervised Learning
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan
Fair Machine Learning Non Convex Optimization Fair Predictor Balanced Loss

November 3, 2023

Equal Opportunity of Coverage in Fair Regression
Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S. Yu
Predictive Uncertainty Fair Machine Learning Higher Coverage Rate Substantive Equality Fair Regression Uncertainty Aware Fairness

October 19, 2023

October 3, 2023

FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling
Yan Luo, Muhammad Osama Khan, Yu Tian, Min Shi, Zehao Dou, Tobias Elze, Yi Fang, Mengyu Wang
Medical Image Fair Machine Learning Fair Algorithm Fairness Datasets Fair Inference Eye Disease Screening

September 29, 2023

Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
Emily Black, Rakshit Naidu, Rayid Ghani, Kit T. Rodolfa, Daniel E. Ho, Hoda Heidari
Algorithmic Fairness Fairness Constraint Fair Machine Learning Practical Guide Easy Tool Research Agenda Algorithmic Unfairness

September 28, 2023

Towards Poisoning Fair Representations
Tianci Liu, Haoyu Wang, Feijie Wu, Hengtong Zhang, Pan Li, Lu Su, Jing Gao
Fair Machine Learning Fair Representation Fair Representation Learning

September 20, 2023

Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework
Sina Baharlouei, Meisam Razaviyayn
Empirical Risk Minimization Fair Machine Learning Robust Fairness Fair Inference

September 12, 2023

Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systems
Ehsan Toreini, Maryam Mehrnezhad, Aad van Moorsel
Procedural Fairness Computation Method Fairness Metric Fair Machine Learning Machine Learning System

June 26, 2023

Insights From Insurance for Fair Machine Learning
Christian Fröhlich, Robert C. Williamson
Machine Learning Procedural Fairness DCU Insight AQ Fair Machine Learning Fairness Notion Interdisciplinary Perspective Insurance Industry

June 15, 2023

Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML
Giang Nguyen, Sumon Biswas, Hridesh Rajan
Machine Learning Bias Mitigation Online autoML Fair Machine Learning Fairness Improvement Accuracy Loss Bias Mitigation Algorithm

June 8, 2023

Reconciling Predictive and Statistical Parity: A Causal Approach
Drago Plecko, Elias Bareinboim
Fair Machine Learning Fairness Notion Causal Approach Fairness Measure Statistical Parity

June 2, 2023

The Flawed Foundations of Fair Machine Learning
Robert Lee Poe, Soumia Zohra El Mestari
Procedural Fairness Fair Machine Learning Flawed Foundation Group Disparity Data Ethic

May 31, 2023

Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations
Madeleine Waller, Odinaldo Rodrigues, Oana Cocarascu
Timely Survey Bias Mitigation Movie Recommendation Binary Classification Fair Machine Learning Decision Making System

May 22, 2023

Causality-Aided Trade-off Analysis for Machine Learning Fairness
Zhenlan Ji, Pingchuan Ma, Shuai Wang, Yanhui Li
General Analysis Causal Discovery Fair Machine Learning Fairness Improvement Machine Learning Fairness

May 3, 2023

fairml: A Statistician's Take on Fair Machine Learning Modelling
Marco Scutari
Model Selection Parameter Estimation Fair Machine Learning Fair Model Statistical Task

April 21, 2023

Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
William G. La Cava
New Machine Multi Objective Optimization Fair Machine Learning Fairness Accuracy Fairness Performance Trade