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

September 23, 2022

September 22, 2022

SCALES: From Fairness Principles to Constrained Decision-Making
Sreejith Balakrishnan, Jianxin Bi, Harold Soh
Procedural Fairness Achieving Fairness Ghanaian Musical Scale Equity Context

September 19, 2022

September 16, 2022

Algorithmic decision making methods for fair credit scoring
Darie Moldovan
Procedural Fairness NCD Method Bias Mitigation Algorithmic Decision Fair Credit Credit Decision

September 15, 2022

September 14, 2022

Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models
Eric Ingram, Furkan Gursoy, Ioannis A. Kakadiaris
Inherent Interpretability Procedural Fairness Recidivism Model

September 13, 2022

September 12, 2022

September 10, 2022

Causal Intervention for Fairness in Multi-behavior Recommendation
Xi Wang, Wenjie Wang, Fuli Feng, Wenge Rong, Chuantao Yin, Zhang Xiong
Recommender System Procedural Fairness Recommendation Accuracy Item Popularity Causal Intervention Popularity Bias Multi Behavior

September 8, 2022

Who Pays? Personalization, Bossiness and the Cost of Fairness
Paresha Farastu, Nicholas Mattei, Robin Burke
Procedural Fairness Hidden CoST Fairness Objective Dominance Hierarchy User Oriented Fairness Fairness Aware Recommendation Fairness Aware Recommender System

September 2, 2022

Procedural Fairness

Papers

Doubly Fair Dynamic Pricing

Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making

SCALES: From Fairness Principles to Constrained Decision-Making

High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness

Fairness and robustness in anti-causal prediction

Fairness in Face Presentation Attack Detection

Fairness on Synthetic Visual and Thermal Mask Images

Algorithmic decision making methods for fair credit scoring

Adaptive Fairness Improvement Based on Causality Analysis

iFlipper: Label Flipping for Individual Fairness

Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models

Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks

Fast online ranking with fairness of exposure

It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games

Fairness in Forecasting of Observations of Linear Dynamical Systems

Causal Intervention for Fairness in Multi-behavior Recommendation

Who Pays? Personalization, Bossiness and the Cost of Fairness

Cubic-Regularized Newton for Spectral Constrained Matrix Optimization and its Application to Fairness

A Discussion of Discrimination and Fairness in Insurance Pricing