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

November 7, 2023

When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks
Huan Tian, Guangsheng Zhang, Bo Liu, Tianqing Zhu, Ming Ding, Wanlei Zhou
Procedural Fairness Membership Inference Attack Privacy Policy Model Fairness Fairness Method Privacy Threat

November 6, 2023

November 5, 2023

November 2, 2023

October 30, 2023

Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data
Mayana Pereira, Meghana Kshirsagar, Sumit Mukherjee, Rahul Dodhia, Juan Lavista Ferres, Rafael de Sousa
Synthetic Data Procedural Fairness Tabular Data Synthetic Data Generation Synthetic Dataset Task Utility Machine Learning Pipeline

October 27, 2023

MCRAGE: Synthetic Healthcare Data for Fairness
Keira Behal, Jiayi Chen, Caleb Fikes, Sophia Xiao
Procedural Fairness Class Imbalance Medical Datasets Synthetic Medical Data Synthetic Electronic Health Record Imbalanced Medical

October 26, 2023

October 25, 2023

On the Interplay between Fairness and Explainability
Stephanie Brandl, Emanuele Bugliarello, Ilias Chalkidis
Procedural Fairness High Explainability Human Annotated Rationale

October 24, 2023

On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms
Surbhi Mittal, Kartik Thakral, Richa Singh, Mayank Vatsa, Tamar Glaser, Cristian Canton Ferrer, Tal Hassner
Artificial Intelligence Procedural Fairness Privacy Policy Trustworthy Artificial Intelligence Responsible Machine Learning Normative System Dataset Documentation

October 23, 2023

Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju
Large Language Model Procedural Fairness Bias Mitigation Social Bias Traditional Machine Learning Tabular Data Classification

October 20, 2023

October 18, 2023

October 17, 2023

Procedural Fairness

Papers

When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks

The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing

FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System

ELEGANT: Certified Defense on the Fairness of Graph Neural Networks

Procedural Fairness Through Decoupling Objectionable Data Generating Components

Improving Fairness using Vision-Language Driven Image Augmentation

Dynamic Fair Federated Learning Based on Reinforcement Learning

Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data

MCRAGE: Synthetic Healthcare Data for Fairness

Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models

fairret: a Framework for Differentiable Fairness Regularization Terms

On the Interplay between Fairness and Explainability

On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms

Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications

Adversarial Attacks on Fairness of Graph Neural Networks

FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation

Exploring Fairness in Pre-trained Visual Transformer based Natural and GAN Generated Image Detection Systems and Understanding the Impact of Image Compression in Fairness

Evaluating the Fairness of Discriminative Foundation Models in Computer Vision

Group-blind optimal transport to group parity and its constrained variants

Understanding Fairness Surrogate Functions in Algorithmic Fairness