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

January 13, 2023

Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities
Samer B. Nashed, Justin Svegliato, Su Lin Blodgett
Procedural Fairness Decision Making Emerging Opportunity Continuum Limit Critical Lesson Algorithmic Fairness Sequential Decision Making Ethical Decision Making Decision Making Algorithm

January 12, 2023

January 4, 2023

On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations
Wenlong Deng, Yuan Zhong, Qi Dou, Xiaoxiao Li
Procedural Fairness Medical Image Classification Representation Space Sensitive Attribute Orthogonal Basis

January 2, 2023

Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs
Sourav Mondal, Marco Ruffini
Procedural Fairness Resource Allocation Open Radio Access Network Auction Based RAN Architecture Passive Optical Network Network Function

December 29, 2022

Properties of Group Fairness Metrics for Rankings
Tobias Schumacher, Marlene Lutz, Sandipan Sikdar, Markus Strohmaier
Procedural Fairness Group Fairness Partial Ranking Many Property Fair Ranking Group Fairness Metric

December 21, 2022

Consistent Range Approximation for Fair Predictive Modeling
Jiongli Zhu, Sainyam Galhotra, Nazanin Sabri, Babak Salimi
Procedural Fairness Predictive Model Biased Data Consistent Range Approximation

December 18, 2022

A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness
Tiantian Feng, Rajat Hebbar, Nicholas Mehlman, Xuan Shi, Aditya Kommineni, and Shrikanth Narayanan
Machine Learning Procedural Fairness Privacy Policy Human SAFETY Machine Learning System Trustworthy Machine Learning

December 16, 2022

Provable Fairness for Neural Network Models using Formal Verification
Giorgian Borca-Tasciuc, Xingzhi Guo, Stanley Bak, Steven Skiena
Machine Learning Model Procedural Fairness Neural Network Model Formal Verification Formal Proof Rethinking Fairness

December 13, 2022

December 9, 2022

Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyond
Philipp Hacker, Johann Cordes, Janina Rochon
Artificial Intelligence Procedural Fairness Transparency Index AI Act General Data Protection Regulation AI Regulation

December 7, 2022

Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations
Yuying Zhao, Yu Wang, Tyler Derr
Procedural Fairness High Explainability Fairness Perception

December 4, 2022

Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, Phebe Vayanos
Procedural Fairness Resource Allocation Metric Library Fairness Metric Self Compatibility Fairness Property Allocation Policy

December 1, 2022

Fairness in Multi-Agent Planning
Alberto Pozanco, Daniel Borrajo
Procedural Fairness Multi Agent Planning Efficient Compilation Goal Oriented Task

November 30, 2022

Welfare and Fairness in Multi-objective Reinforcement Learning
Zimeng Fan, Nianli Peng, Muhang Tian, Brandon Fain
Procedural Fairness Q Learning Multi Objective Reinforcement Learning Vector Valued Social Welfare Seller Side Outcome Fairness

November 23, 2022

November 21, 2022

Procedural Fairness

Papers

Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities

Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning

Fairly Private: Investigating The Fairness of Visual Privacy Preservation Algorithms

On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations

Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs

Properties of Group Fairness Metrics for Rankings

Consistent Range Approximation for Fair Predictive Modeling

A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness

Provable Fairness for Neural Network Models using Formal Verification

Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting

FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data

Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyond

Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations

Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results

Fairness in Multi-Agent Planning

Welfare and Fairness in Multi-objective Reinforcement Learning

On the Complexity of Finding a Diverse and Representative Committee using a Monotone, Separable Positional Multiwinner Voting Rule

Fairly Allocating Utility in Constrained Multiwinner Elections

Equality of Effort via Algorithmic Recourse

Fairness Increases Adversarial Vulnerability