Fairness Analysis

Fairness analysis in machine learning aims to identify and mitigate biases in algorithms that lead to unfair or discriminatory outcomes across different demographic groups. Current research focuses on developing and applying fairness metrics, exploring bias mitigation techniques within various model architectures (including deep learning, graph neural networks, and foundation models), and investigating the impact of data imbalances and missing sensitive attributes. This work is crucial for ensuring equitable outcomes in high-stakes applications like healthcare, finance, and autonomous systems, and for advancing the development of more responsible and ethical AI.

Papers

September 28, 2024

Fairness Analysis with Shapley-Owen Effects
Harald Ruess
Fairness Analysis Polynomial Chaos Expansion Spectral Decomposition Balanced Truncation

September 6, 2024

Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges
Parameswaran Kamalaruban, Yulu Pi, Stuart Burrell, Eleanor Drage, Piotr Skalski, Jason Wong, David Sutton
Technical Challenge Fraud Detection Fairness Metric Fairness Analysis Algorithm Auditing Bias Auditing

August 28, 2024

Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data
Dilermando Queiroz, André Anjos, Lilian Berton
Bias Mitigation Fairness Assessment Fairness Analysis Demographic Data Equitable Healthcare System

June 25, 2024

Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts
Xuyang Wu, Yuan Wang, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang
Large Vision Language Model Complex Prompt Visual Understanding Socio Demographic Fairness Analysis Visual Bias Fairness Benchmark Datasets

March 29, 2024

FairCLIP: Harnessing Fairness in Vision-Language Learning
Yan Luo, Min Shi, Muhammad Osama Khan, Muhammad Muneeb Afzal, Hao Huang, Shuaihang Yuan, Yu Tian, Luo Song, Ava Kouhana, Tobias Elze, Yi Fang, Mengyu Wang
Fairness Analysis Medical Vision

March 27, 2024

Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions
Zainab Al-Zanbouri, Gauri Sharma, Shaina Raza
Machine Learning Machine Learning Model Healthcare System Fair Machine Learning Generalized Linear Model Health Disparity Fairness Analysis

January 3, 2024

Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data
Sofia Yfantidou, Dimitris Spathis, Marios Constantinides, Athena Vakali, Daniele Quercia, Fahim Kawsar
Self Supervised Supervised Learning Inductive Bias Self Supervision Supervised Fine Tuning Sequential Data Fine Tuning Strategy Fairness Analysis

October 19, 2023

Exploring Graph Neural Networks for Indian Legal Judgment Prediction
Mann Khatri, Mirza Yusuf, Yaman Kumar, Rajiv Ratn Shah, Ponnurangam Kumaraguru
Graph Neural Network Link Prediction Legal Judgment Prediction Fairness Analysis

October 4, 2023

Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data
Son Nguyen, Adam Wang, Albert Montillo
Deep Learning Procedural Fairness Mixed Effect Distribution Data Mixed Effect Model Fairness Analysis Cluster Specific Random Effect Fairness Attribute Non IID

August 5, 2023

Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems
Xinyue Li, Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Ying Zhang, Xuanzhe Liu
Autonomous Driving Absolute Stance Bias Pedestrian Detection Fairness Analysis Wheel Development Pedestrian Detector

May 9, 2023

Towards unraveling calibration biases in medical image analysis
María Agustina Ricci Lara, Candelaria Mosquera, Enzo Ferrante, Rodrigo Echeveste
Medical Image Analysis Algorithmic Fairness Fairness Research Fairness Analysis Bias Calibration

May 8, 2023

Distribution-aware Fairness Test Generation
Sai Sathiesh Rajan, Ezekiel Soremekun, Yves Le Traon, Sudipta Chattopadhyay
Image Datasets Recognition System Fairness Analysis Fairness Testing Class Fairness

April 10, 2023

Analysing Fairness of Privacy-Utility Mobility Models
Yuting Zhan, Hamed Haddadi, Afra Mashhadi
Fairness Metric Privacy Utility Privacy Utility Trade Fairness Analysis Fairness Estimation

February 9, 2023

On Fairness and Stability: Is Estimator Variance a Friend or a Foe?
Falaah Arif Khan, Denys Herasymuk, Julia Stoyanovich
Procedural Fairness Core Stability Fairness Metric Fairness Measure Fairness Analysis Enemy Spotted Variance Estimation

October 6, 2022

Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes
Zhaowei Zhu, Yuanshun Yao, Jiankai Sun, Hang Li, Yang Liu
Procedural Fairness Sensitive Attribute Proxy Dataset Fairness Analysis Proxy Model

September 19, 2022

Fairness in Face Presentation Attack Detection
Meiling Fang, Wufei Yang, Arjan Kuijper, Vitomir Struc, Naser Damer
Procedural Fairness Face Recognition Presentation Attack Detection Face Presentation Attack Detection Fairness Analysis Fair Adversarial

August 11, 2022

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
Ying Xu, Philipp Terhörst, Kiran Raja, Marius Pedersen
Deepfake Detection Fairness Analysis Deepfake Detection Method Deepfake Datasets

June 8, 2022

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective
Zeyu Tang, Jiji Zhang, Kun Zhang
Machine Learning Timely Survey Procedural Fairness Visual Perspective Tutorial Review Fairness Notion Fairness Analysis

April 18, 2022

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness
McKane Andrus, Sarah Villeneuve
Procedural Fairness Algorithmic Fairness Risk Sensitive Fair Algorithm Fairness Analysis Demographic Data Demographic Fairness

March 14, 2022

FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing
Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, Anders Søgaard
Pre Trained Language Model Procedural Fairness Multilingual Benchmark Fairness Improvement Robust Fine Tuning Fairness Analysis Legal NLP

Fairness Analysis

Papers

Fairness Analysis with Shapley-Owen Effects

Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges

Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data

Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts

FairCLIP: Harnessing Fairness in Vision-Language Learning

Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data

Exploring Graph Neural Networks for Indian Legal Judgment Prediction

Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data

Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems

Towards unraveling calibration biases in medical image analysis

Distribution-aware Fairness Test Generation

Analysing Fairness of Privacy-Utility Mobility Models

On Fairness and Stability: Is Estimator Variance a Friend or a Foe?

Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes

Fairness in Face Presentation Attack Detection

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

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

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing