Fairness Aware Machine

Fairness-aware machine learning aims to develop algorithms that make unbiased predictions, avoiding discrimination against specific groups defined by sensitive attributes like race or gender. Current research focuses on mitigating bias through various techniques, including data augmentation, distributionally robust optimization, and fairness-aware model architectures such as boosting ensembles and neural additive models, often addressing challenges posed by imbalanced datasets and limited sensitive attribute information. This field is crucial for ensuring ethical and equitable outcomes in high-stakes applications like healthcare, finance, and criminal justice, driving advancements in both algorithmic design and the understanding of fairness itself.

Papers

October 21, 2024

Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium
Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, AmirArsalan Rajabi, Aida Tayebi, Ivan Garibay, Ozlem Ozmen Garibay
Neural Network Bilevel Optimization Fairness Accuracy Fairness Aware Machine Achieving Fairness Fairness Objective Stackelberg Equilibrium Fairness Method

October 17, 2024

A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes
Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor Awad
Human in the Loop Fairness Metric Model Fairness Complexity Level Fairness Aware Machine Fairness Objective

October 12, 2024

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton
LeArning Abstract Procedural Fairness Fairness Aware Machine Fairness Assessment Fairness Measure Fairness Datasets

May 15, 2024

Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching
Jiaee Cheong, Micol Spitale, Hatice Gunes
Procedural Fairness Affective Computing Fairness Aware Machine ML Fairness Robot Health

May 2, 2024

Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng, Xiangnan He
Movie Recommendation Distributionally Robust Optimization Sensitive Attribute Fairness Aware Machine Protected Attribute

March 5, 2024

The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
Mercy Asiedu, Awa Dieng, Iskandar Haykel, Negar Rostamzadeh, Stephen Pfohl, Chirag Nagpal, Maria Nagawa, Abigail Oppong, Sanmi Koyejo, Katherine Heller
Artificial Intelligence Procedural Fairness Fairness Notion Fairness Aware Machine Mixed Method

February 29, 2024

To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
Cyrus Cousins, I. Elizabeth Kumar, Suresh Venkatasubramanian
Fair Machine Learning Fairness Aware Machine Rademacher Complexity Regularization Effect Tight Bound Fair Learning Fair Training Shared Model

February 28, 2024

On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms
Haoyu Lei, Amin Gohari, Farzan Farnia
Inductive Bias Fair Machine Learning Sensitive Attribute FAir Classifier Fairness Aware Machine Socio Demographic Fair Learning

February 25, 2024

Distribution-Free Fair Federated Learning with Small Samples
Qichuan Yin, Zexian Wang, Junzhou Huang, Huaxiu Yao, Linjun Zhang
Model Training Fairness Aware Machine Distribution Free Client Heterogeneity Single Sample FAIR Federated Learning

February 2, 2024

Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Minglai Shao, Dong Li, Chen Zhao, Xintao Wu, Yujie Lin, Qin Tian
Timely Survey Machine Learning Model Distribution Shift Fairness Aware Machine Biased Prediction Reliable Prediction

December 31, 2023

Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction
Alexandra Kakadiaris
Procedural Fairness Class Imbalance Binary Classifier Medical Information Mart for Intensive XGBoost Model Fairness Aware Machine Baseline Algorithm

November 21, 2023

Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
Xuan Zhao, Simone Fabbrizzi, Paula Reyero Lobo, Siamak Ghodsi, Klaus Broelemann, Steffen Staab, Gjergji Kasneci
Bias Mitigation Wasserstein Distance Fairness Aware Machine Representation Bias Adversarial Reweighting

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

September 11, 2023

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

July 31, 2023

A Suite of Fairness Datasets for Tabular Classification
Martin Hirzel, Michael Feffer
Procedural Fairness Machine Learning Classifier Fairness Aware Machine Software Suite Fairness Datasets Tabular Classification

July 17, 2023

Learning for Counterfactual Fairness from Observational Data
Jing Ma, Ruocheng Guo, Aidong Zhang, Jundong Li
LeArning Abstract Observational Data Causal Discovery Method Counterfactual Fairness Fairness Aware Machine Fair Prediction

April 27, 2023

Fairness Uncertainty Quantification: How certain are you that the model is fair?
Abhishek Roy, Prasant Mohapatra
Full Model Stochastic Gradient Descent Group Fairness Fairness Aware Machine Uncertainty Aware Fairness

February 12, 2023

Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview
Arjun Roy, Jan Horstmann, Eirini Ntoutsi
Machine Learning Comparative Study Legal Text Fairness Aware Machine Sequential Classifier

January 17, 2023

Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance
Dangxing Chen, Luyao Zhang
Empirical Study Social Science Fairness Aware Machine Partial Monotonicity Neural Additive Model Fair Algorithm Monotonicity Property

December 23, 2022

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
Yao Yao, Qihang Lin, Tianbao Yang
Fairness Constraint Area Under the ROC Curve Fairness Aware Machine Fair Predictor Stochastic Approach

Fairness Aware Machine

Papers

Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium

A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning

Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching

Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach

The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa

To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models

On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms

Distribution-Free Fair Federated Learning with Small Samples

Supervised Algorithmic Fairness in Distribution Shifts: A Survey

Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction

Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation

Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes

Re-formalization of Individual Fairness

A Suite of Fairness Datasets for Tabular Classification

Learning for Counterfactual Fairness from Observational Data

Fairness Uncertainty Quantification: How certain are you that the model is fair?

Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview

Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints