Fairness Performance Trade

Fairness-performance trade-offs in machine learning explore the inherent tension between achieving high predictive accuracy and ensuring equitable outcomes across different demographic groups. Current research focuses on developing algorithms and frameworks to quantify and optimize this trade-off, often employing multi-objective optimization techniques and investigating the interplay between global and local fairness metrics within various model architectures, including federated learning and those addressing causal relationships between sensitive attributes and predictions. This research is crucial for mitigating algorithmic bias and promoting fairness in high-stakes applications like loan applications, healthcare, and criminal justice, ultimately aiming to create more equitable and trustworthy AI systems.

Papers

August 27, 2024

Examining the Interplay Between Privacy and Fairness for Speech Processing: A Review and Perspective
Anna Leschanowsky, Sneha Das
Procedural Fairness Visual Perspective Privacy Policy Bias Mitigation Speech Processing Fairness Performance Trade

August 19, 2024

The Fairness-Quality Trade-off in Clustering
Rashida Hakim, Ana-Andreea Stoica, Christos H. Papadimitriou, Mihalis Yannakakis
Data Clustering Hierarchical Clustering Pareto Front Fairness Objective Fairness Performance Trade

August 15, 2024

Federated Fairness Analytics: Quantifying Fairness in Federated Learning
Oscar Dilley, Juan Marcelo Parra-Ullauri, Rasheed Hussain, Dimitra Simeonidou
Procedural Fairness Fairness Performance Trade

April 15, 2024

Utility-Fairness Trade-Offs and How to Find Them
Sepehr Dehdashtian, Bashir Sadeghi, Vishnu Naresh Boddeti
Fairness Performance Trade Demographic Fairness Fairness Utility Trade Fair Utility

March 22, 2024

Balancing Fairness and Efficiency in Energy Resource Allocations
Jiayi Li, Matthew Motoki, Baosen Zhang
High Efficiency Resource Allocation Balancing Fairness Fairness Performance Trade Efficient Allocation Fair Resource Allocation

July 21, 2023

Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
Faisal Hamman, Sanghamitra Dutta
Group Fairness Information Theoretic Local Explanation Information Decomposition Fairness Performance Trade Local Fairness

May 23, 2023

FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software
Ying Xiao, Shangwen Wang, Sicen Liu, Dingyuan Xue, Xian Zhan, Yepang Liu
Bias Mitigation Mitigating Bias Biased Datasets Physical Activity Causal Approach Fairness Performance Trade Machine Learning Software

April 21, 2023

Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
William G. La Cava
New Machine Multi Objective Optimization Fair Machine Learning Fairness Accuracy Fairness Performance Trade

February 16, 2023

Preventing Discriminatory Decision-making in Evolving Data Streams
Zichong Wang, Nripsuta Saxena, Tongjia Yu, Sneha Karki, Tyler Zetty, Israat Haque, Shan Zhou, Dukka Kc, Ian Stockwell, Albert Bifet, Wenbin Zhang
Fairness Constraint Fair Machine Learning Biased Datasets Fairness Performance Trade Stream Learning Discriminatory Decision

February 4, 2023

Matrix Estimation for Individual Fairness
Cindy Y. Zhang, Sarah H. Cen, Devavrat Shah
Algorithmic Fairness Individual Fairness Fairness Performance Trade Matrix Estimation

January 31, 2023

Superhuman Fairness
Omid Memarrast, Linh Vu, Brian Ziebart
Fair Machine Learning Fairness Measure Fairness Performance Trade Superhuman Performance

October 18, 2022

Towards Fair Classification against Poisoning Attacks
Han Xu, Xiaorui Liu, Yuxuan Wan, Jiliang Tang
Poisoning Attack Fair Classification Fairness Aware Protected Attribute Fairness Performance Trade Malicious Training

July 7, 2022

A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers
Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Mark Harman
Empirical Study Bias Mitigation Machine Learning Classifier Fairness Performance Trade Machine Bias

May 20, 2022

The Fairness of Credit Scoring Models
Christophe Hurlin, Christophe Pérignon, Sébastien Saurin
Procedural Fairness Algorithmic Fairness Financial Market Fairness Performance Trade Credit Scoring Model Scoring Model

April 15, 2022

Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection
Venelin Kovatchev, Soumyajit Gupta, Anubrata Das, Matthew Lease
Multi Objective Optimization Hate Speech Detection Group Fairness Pareto Optimal Fairness Performance Trade Optimal Accuracy

January 12, 2022

Blackbox Post-Processing for Multiclass Fairness
Preston Putzel, Scott Lee
Machine Learning Model Black Box Fair Classification Fairness Performance Trade Class Fairness