Fairness Constraint

Fairness constraints in machine learning aim to mitigate algorithmic bias by ensuring equitable outcomes across different demographic groups. Current research focuses on developing algorithms and model architectures that incorporate fairness metrics (e.g., demographic parity, equal opportunity) into the learning process, often addressing the trade-off between fairness and accuracy through techniques like constrained optimization, re-weighting, and data augmentation. This field is crucial for ensuring responsible AI development, impacting various applications from loan approvals and hiring to healthcare and criminal justice by promoting equitable and trustworthy decision-making systems.

Papers

February 17, 2023

Designing Equitable Algorithms
Alex Chohlas-Wood, Madison Coots, Sharad Goel, Julian Nyarko
Practical Algorithm Procedural Fairness Fairness Constraint Fair Algorithm Predictive Algorithm

February 16, 2023

February 13, 2023

The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
Andrew Bell, Lucius Bynum, Nazarii Drushchak, Tetiana Herasymova, Lucas Rosenblatt, Julia Stoyanovich
Procedural Fairness Full Potential Practice Mode Algorithmic Fairness Fairness Constraint Impossibility Theorem

February 6, 2023

Fair Minimum Representation Clustering
Connor Lawless, Oktay Gunluk
K Mean Fairness Constraint Fair Clustering

February 5, 2023

The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default
Brent Mittelstadt, Sandra Wachter, Chris Russell
Procedural Fairness Fairness Constraint Fair Machine Learning Fairness Measure Egalitarian Fairness Multiple Fairness Metric Default Policy

January 29, 2023

Fair Decision-making Under Uncertainty
Wenbin Zhang, Jeremy C. Weiss
Procedural Fairness High Uncertainty Anticipation Fairness Constraint Fairness Notion Algorithmic Decision Fair Decision Fair Prediction

January 28, 2023

Pragmatic Fairness: Developing Policies with Outcome Disparity Control
Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva
Optimal Policy Fairness Constraint Prior Policy

January 27, 2023

Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
Hao Wang, Luxi He, Rui Gao, Flavio P. Calmon
Machine Learning Fairness Constraint Fundamental Limit Fairness Intervention Epistemic State Non Gaussian Aleatoric Uncertainty

January 5, 2023

Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms
Yanhao Wang, Michael Mathioudakis, Jia Li, Francesco Fabbri
Fairness Constraint Integer Programming Approximation Algorithm Diversity Maximization

December 26, 2022

Bias Mitigation Framework for Intersectional Subgroups in Neural Networks
Narine Kokhlikyan, Bilal Alsallakh, Fulton Wang, Vivek Miglani, Oliver Aobo Yang, David Adkins
Neural Network Bias Mitigation Fairness Constraint Fairness Aware Learning

December 23, 2022

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
Yao Yao, Qihang Lin, Tianbao Yang
Fairness Constraint Area Under the ROC Curve Fairness Aware Machine Fair Predictor Stochastic Approach

November 30, 2022

Fair Ranking with Noisy Protected Attributes
Anay Mehrotra, Nisheeth K. Vishnoi
Fairness Constraint Fair Ranking Protected Attribute Group Fairness Constraint

November 23, 2022

Vertical Federated Learning: Concepts, Advances and Challenges
Yang Liu, Yan Kang, Tianyuan Zou, Yanhong Pu, Yuanqin He, Xiaozhou Ye, Ye Ouyang, Ya-Qin Zhang, Qiang Yang
Technical Challenge Vertical Federated Learning Concept Identification Fairness Constraint Privacy Attack

November 8, 2022

Reinforcement Learning with Stepwise Fairness Constraints
Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes
Reinforcement Learning Fairness Constraint Tabular Reinforcement Learning Sequential Decision Making Policy

October 18, 2022

Consistent Multiclass Algorithms for Complex Metrics and Constraints
Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal
Participation Constraint Fairness Constraint Complex Valued Consistency Model Confusion Matrix Multi Class Classification Task Multiclass Learning

October 10, 2022

FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
Shubham Sharma, Jette Henderson, Joydeep Ghosh
Expert Knowledge Interpretable Model Fairness Metric Fairness Constraint Trustworthy Machine Learning Adaptive Mixture

September 23, 2022

Doubly Fair Dynamic Pricing
Jianyu Xu, Dan Qiao, Yu-Xiang Wang
Procedural Fairness Fairness Constraint Online Learning Algorithm Dynamic Pricing Dynamic Pricing Algorithm

September 16, 2022

FairGBM: Gradient Boosting with Fairness Constraints
André F Cruz, Catarina Belém, Sérgio Jesus, João Bravo, Pedro Saleiro, Pedro Bizarro
Natural Gradient Fairness Constraint Fair Machine Learning Gradient Based Optimization Dual Ascent

September 15, 2022

Multicalibrated Regression for Downstream Fairness
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
Fairness Constraint Post Processing Regression Function Multicalibrated Predictor