Procedural Fairness

Procedural fairness in artificial intelligence focuses on ensuring that AI systems make decisions equitably across different demographic groups, mitigating biases that can lead to discriminatory outcomes. Current research emphasizes developing and evaluating fairness-aware algorithms and models, including those based on adversarial learning, data augmentation techniques like mixup, and distributionally robust optimization, across various applications like healthcare, process analytics, and recommender systems. This research is crucial for building trustworthy AI systems and addressing societal concerns about algorithmic bias, impacting both the development of ethical AI guidelines and the practical deployment of AI in sensitive domains.

Papers

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 26, 2022

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
Bang An, Zora Che, Mucong Ding, Furong Huang
Procedural Fairness Distribution Shift Group Fairness Algorithmic Fairness Model Fairness Fair Model Fairness Regularization

June 22, 2022

June 20, 2022

Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R\'enyi Measures
Adam Gronowski, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina
Procedural Fairness Information Bottleneck Fairness Constraint Fair Representation Classification Application R\'enyi Entropy

June 15, 2022

Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, P. Winston Michalak, Shahab Asoodeh, Flavio P. Calmon
Procedural Fairness Fairness Accuracy Older Adult Group Fairness Constraint Multi Class Classification Task Pre Trained Classifier Class Prediction Fair Classifier

June 14, 2022

June 8, 2022

June 7, 2022

Fairness and Unfairness in Binary and Multiclass Classification: Quantifying, Calculating, and Bounding
Sivan Sabato, Eran Treister, Elad Yom-Tov
Procedural Fairness Binary Code General Bound Multiclass Classification FAir Classifier Equalized Odds Algorithm Auditing

June 6, 2022

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, James Zou
Procedural Fairness Simple Classifier Imbalanced Data Fairness Aware Machine Fairness Learning Fairness Aware Training

June 1, 2022

FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks
Kiarash Mohammadi, Aishwarya Sivaraman, Golnoosh Farnadi
Neural Network Practical Algorithm Procedural Fairness Inductive Bias Verification Task Fairness Constraint Fairness Notion Dynamic Fairness Fairness Problem

May 30, 2022

Uncertainty Quantification for Fairness in Two-Stage Recommender Systems
Lequn Wang, Thorsten Joachims
Procedural Fairness Uncertainty Quantification Biased Feedback Two Stage Recommender

May 27, 2022

May 26, 2022

Fairness in Recommendation: Foundations, Methods and Applications
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, Yongfeng Zhang
Financial Application Recommender System Procedural Fairness NCD Method Effective Recommendation Flawed Foundation Fairness Research Recommendation Fairness

Procedural Fairness

Papers

Fair Machine Learning in Healthcare: A Review

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

A Disability Lens towards Biases in GPT-3 Generated Open-Ended Languages

Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education

Context matters for fairness -- a case study on the effect of spatial distribution shifts

Constant-Factor Approximation Algorithms for Socially Fair $k$-Clustering

FairGrad: Fairness Aware Gradient Descent

Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R\'enyi Measures

Beyond Adult and COMPAS: Fairness in Multi-Class Prediction

Measuring Representational Harms in Image Captioning

Causal Discovery for Fairness

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

Fairness and Unfairness in Binary and Multiclass Classification: Quantifying, Calculating, and Bounding

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data

FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks

Uncertainty Quantification for Fairness in Two-Stage Recommender Systems

Prototype Based Classification from Hierarchy to Fairness

Fairness and Welfare Quantification for Regret in Multi-Armed Bandits

Fairness in Recommendation: Foundations, Methods and Applications