Fairness Metric

Fairness metrics are quantitative measures used to assess and mitigate bias in machine learning models and other decision-making systems, aiming to ensure equitable outcomes across different demographic groups. Current research focuses on developing new metrics that address limitations of existing ones, particularly concerning uncertainty, data drift, and the imbalanced nature of real-world datasets; this includes exploring various model architectures and algorithms, such as those based on re-weighting, post-processing, and generative models, to improve fairness while maintaining accuracy. The development and application of robust fairness metrics are crucial for building trustworthy and equitable AI systems across diverse domains, impacting both the ethical development of AI and its practical deployment in sensitive areas like healthcare and finance.

Papers

November 10, 2024

A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
Caleb J.S. Barr, Olivia Erdelyi, Paul D. Docherty, Randolph C. Grace
Machine Learning Machine Learning Model Procedural Fairness Narrative Review View Selection Fairness Metric Fairness Measure

November 7, 2024

November 3, 2024

Mitigating Matching Biases Through Score Calibration
Mohammad Hossein Moslemi, Mostafa Milani
Bias Mitigation Fairness Metric Equalized Odds Record Linkage Score Calibration

October 22, 2024

FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation
Rohan Sukumaran, Aarash Feizi, Adriana Romero-Sorian, Golnoosh Farnadi
Fine Tuning Parameter Efficient Fine Tuning Adaptation Concern Vision Model Bias Mitigation Fairness Metric Fast LoRA

October 17, 2024

A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes
Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor Awad
Human in the Loop Fairness Metric Model Fairness Complexity Level Fairness Aware Machine Fairness Objective

October 16, 2024

Using Protected Attributes to Consider Fairness in Multi-Agent Systems
Gabriele La Malfa, Jie M. Zhang, Michael Luck, Elizabeth Black
Procedural Fairness Multi Agent System Algorithmic Fairness Fairness Metric Multi Agent Setting Counterfactual Fairness Protected Attribute

September 19, 2024

September 14, 2024

Using Synthetic Data to Mitigate Unfairness and Preserve Privacy through Single-Shot Federated Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
Synthetic Data Privacy Policy Fairness Metric Weight Aggregation Fairness Aware Aggregation

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

September 5, 2024

Understanding Fairness Metrics in Recommender Systems: A Healthcare Perspective
Veronica Kecki, Alan Said
Recommender System Procedural Fairness Healthcare System Algorithmic Fairness Fairness Metric

August 29, 2024

A Catalog of Fairness-Aware Practices in Machine Learning Engineering
Gianmario Voria, Giulia Sellitto, Carmine Ferrara, Francesco Abate, Andrea De Lucia, Filomena Ferrucci, Gemma Catolino, Fabio Palomba
Machine Learning Fairness Metric Fairness Aware Achieving Fairness

August 13, 2024

BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning
Yuyang Xue, Junyu Yan, Raman Dutt, Fasih Haider, Jingshuai Liu, Steven McDonagh, Sotirios A. Tsaftaris
Procedural Fairness Group Fairness Fairness Metric Debiasing Method Weight Mask

July 16, 2024

July 15, 2024

An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases
Dylan Bouchard
Large Language Model Procedural Fairness Absolute Stance Bias Fairness Metric LLM Integrated Application LLM Bias Fair Credit Practical Framework Language Model Application

June 25, 2024

June 24, 2024

Efficient k-means with Individual Fairness via Exponential Tilting
Shengkun Zhu, Jinshan Zeng, Yuan Sun, Sheng Wang, Xiaodong Li, Zhiyong Peng
Resource Allocation Fairness Metric Tilting Angle Individual Fairness Fair Clustering

Fairness Metric

Papers

A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning

Comparing Fairness of Generative Mobility Models

ComFairGNN: Community Fair Graph Neural Network

Mitigating Matching Biases Through Score Calibration

FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation

A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes

Using Protected Attributes to Consider Fairness in Multi-Agent Systems

(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers

Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift

Using Synthetic Data to Mitigate Unfairness and Preserve Privacy through Single-Shot Federated Learning

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

Understanding Fairness Metrics in Recommender Systems: A Healthcare Perspective

A Catalog of Fairness-Aware Practices in Machine Learning Engineering

BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning

Rethinking Fair Graph Neural Networks from Re-balancing

EARN Fairness: Explaining, Asking, Reviewing and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders

An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases

Fairness in Social Influence Maximization via Optimal Transport

Fairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness

Efficient k-means with Individual Fairness via Exponential Tilting