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

August 13, 2022

A Novel Regularization Approach to Fair ML
Norman Matloff, Wenxi Zhang
Machine Learning Model Fair Machine Learning Novel Regularization

July 27, 2022

Fairness and Randomness in Machine Learning: Statistical Independence and Relativization
Rabanus Derr, Robert C. Williamson
Machine Learning Procedural Fairness Fair Machine Learning Semantic Structure Inherent Randomness Randomized Algorithm Randomness Factor

July 26, 2022

Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
Ričards Marcinkevičs, Ece Ozkan, Julia E. Vogt
Deep Neural Network Chest X Ray Fair Machine Learning Post Processing Early Screening

July 13, 2022

Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
José Pombal, André F. Cruz, João Bravo, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro
Full Model Fraud Detection Fair Machine Learning Fairness Accuracy Algorithmic Unfairness

July 8, 2022

On the Need and Applicability of Causality for Fair Machine Learning
Rūta Binkytė, Ljupcho Grozdanovski, Sami Zhioua
Procedural Fairness Causal Pattern Community Need Fair Machine Learning Fairness Evaluation Applicability Study Causal Prediction Causal View

July 5, 2022

Developing a Philosophical Framework for Fair Machine Learning: Lessons From The Case of Algorithmic Collusion
James Michelson
Procedural Fairness Case Relevance Critical Lesson Fairness Metric Fair Machine Learning Algorithmic Collusion Philosophical Framework

June 29, 2022

Fair Machine Learning in Healthcare: A Review
Qizhang Feng, Mengnan Du, Na Zou, Xia Hu
Procedural Fairness Narrative Review Healthcare System Fairness Metric Fair Machine Learning Health Disparity

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

June 6, 2022

Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics
Joachim Baumann, Corinna Hertweck, Michele Loi, Christoph Heitz
Fairness Metric Fair Machine Learning Flawed Foundation Anti Unification Fairness Objective Group Fairness Metric

May 23, 2022

PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
Sikha Pentyala, Nicola Neophytou, Anderson Nascimento, Martine De Cock, Golnoosh Farnadi
Differential Privacy Group Fairness Fair Machine Learning Secure Multiparty Computation

May 15, 2022

Fair Bayes-Optimal Classifiers Under Predictive Parity
Xianli Zeng, Edgar Dobriban, Guang Cheng
Fair Machine Learning Equalized Odds Optimal Fair

May 11, 2022

A Survey on Fairness for Machine Learning on Graphs
Charlotte Laclau, Christine Largeron, Manvi Choudhary
Machine Learning Timely Survey Graph Drawing Procedural Fairness Algorithmic Fairness Fair Machine Learning Graph Mining Graph Mining Task

May 5, 2022

Subverting Fair Image Search with Generative Adversarial Perturbations
Avijit Ghosh, Matthew Jagielski, Christo Wilson
Native Robustness Fair Machine Learning Intersectional Fairness

April 12, 2022

Breaking Fair Binary Classification with Optimal Flipping Attacks
Changhun Jo, Jy-yong Sohn, Kangwook Lee
Fairness Constraint Fair Machine Learning FAir Classifier Flip Attack

February 20, 2022

Bayes-Optimal Classifiers under Group Fairness
Xianli Zeng, Edgar Dobriban, Guang Cheng
Group Fairness Fair Machine Learning Fairness Accuracy Bayes Classifier Group Fairness Constraint

February 17, 2022

Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning
Hilde Weerts, Lambèr Royakkers, Mykola Pechenizkiy
Fairness Metric Fair Machine Learning Multiple Meaning Annotated End State Fairness Aware Machine

February 8, 2022

Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions
Bhavya Ghai, Mihir Mishra, Klaus Mueller
Fairness Metric Fair Machine Learning Fairness Intervention Cumulative Water Heater

January 19, 2022

Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows
Wiebke Toussaint, Aaron Yi Ding, Fahim Kawsar, Akhil Mathur
Fair Machine Learning Real World Office Workflow Design Choice Device Use Case Client Availability Device Machine Learning

January 17, 2022

Visual Identification of Problematic Bias in Large Label Spaces
Alex Bäuerle, Aybuke Gul Turker, Ken Burke, Osman Aka, Timo Ropinski, Christina Greer, Mani Varadarajan
Ground Truth Fair Machine Learning Label Space Fairness Assessment ML Fairness

January 13, 2022

The Fairness Field Guide: Perspectives from Social and Formal Sciences
Alycia N. Carey, Xintao Wu
Machine Learning Machine Learning Model Procedural Fairness Synthesized View Fair Machine Learning Rigorous Framework Fairness Toolkits