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

April 8, 2023

Connecting Fairness in Machine Learning with Public Health Equity
Shaina Raza
Machine Learning Procedural Fairness Fair Machine Learning Systematic Bias ML Fairness Health Equity Equitable Framework

April 7, 2023

March 20, 2023

Bias mitigation techniques in image classification: fair machine learning in human heritage collections
Dalia Ortiz Pablo, Sushruth Badri, Erik Norén, Christoph Nötzli
Transfer Learning Image Classification Bias Mitigation Fair Machine Learning Face Dataset

March 15, 2023

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
Machine Learning Procedural Fairness Emerging Opportunity Fair Machine Learning State Aware Guideline Fairness Research Fair Credit Rethinking Fairness

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

February 18, 2023

Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu
Machine Learning Fair Machine Learning Liver Transplant

February 16, 2023

February 14, 2023

When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness
Natasa Krco, Thibault Laugel, Vincent Grari, Jean-Michel Loubes, Marcin Detyniecki
Absolute Stance Bias Bias Mitigation Group Fairness Fair Machine Learning Model Multiplicity Algorithmic Arbitrariness

February 5, 2023

The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default
Brent Mittelstadt, Sandra Wachter, Chris Russell
Procedural Fairness Fairness Constraint Fair Machine Learning Fairness Measure Egalitarian Fairness Multiple Fairness Metric Default Policy

February 2, 2023

Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Akshaj Kumar Veldanda, Ivan Brugere, Sanghamitra Dutta, Alan Mishler, Siddharth Garg
Fairness Metric Fair Machine Learning Sensitive Attribute Fair Classification Fair Model FAir Classifier Hyper Parameter Tuning Biased Classifier

January 31, 2023

Superhuman Fairness
Omid Memarrast, Linh Vu, Brian Ziebart
Fair Machine Learning Fairness Measure Fairness Performance Trade Superhuman Performance

January 30, 2023

Fairness and Accuracy under Domain Generalization
Thai-Hoang Pham, Xueru Zhang, Ping Zhang
Machine Learning Machine Learning Model Procedural Fairness Domain Generalization Fair Machine Learning

November 11, 2022

Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
Tennison Liu, Alex J. Chan, Boris van Breugel, Mihaela van der Schaar
Algorithmic Fairness Fair Machine Learning Sensitive Attribute Practical Approach Fair Predictor Fair Learning

November 4, 2022

Fairness-aware Regression Robust to Adversarial Attacks
Yulu Jin, Lifeng Lai
Adversarial Attack Fair Machine Learning Fair Regression

November 3, 2022

Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study
Pei-Hung Lin, Chunhua Liao, Winson Chen, Tristan Vanderbruggen, Murali Emani, Hailu Xu
Machine Learning Case Study NCD Method Fair Machine Learning High Performance Computing Fairness Assessment Public Model

October 17, 2022

Stochastic Differentially Private and Fair Learning
Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
Differential Privacy Stochastic Way Fair Machine Learning Private Algorithm Fairness Notion Fair Learning

September 16, 2022

FairGBM: Gradient Boosting with Fairness Constraints
André F Cruz, Catarina Belém, Sérgio Jesus, João Bravo, Pedro Saleiro, Pedro Bizarro
Natural Gradient Fairness Constraint Fair Machine Learning Gradient Based Optimization Dual Ascent

August 24, 2022

Enforcing Delayed-Impact Fairness Guarantees
Aline Weber, Blossom Metevier, Yuriy Brun, Philip S. Thomas, Bruno Castro da Silva
Fairness Constraint Fair Machine Learning Long Term Fairness Exact Enforcement Term Fairness Fairness Attribute