Latest Articles for Fairness

New method reduces bias in machine learning while maintaining performance.

Examining bias in AI algorithms affecting medical diagnoses for diverse populations.

A look into federated learning's impact on privacy and fairness.

FairDP offers a solution for ensuring privacy and fairness in machine learning systems.

Examining fairness in distributing goods and chores among individuals with different needs.

A look at fairness and bias in models for detecting online toxic comments.

A new algorithm aims to improve fairness alongside relevance in ranking systems.

A new approach for secure messaging without revealing sender or receiver identities.

Explore the challenges and solutions for concurrent programs in weak memory models.

A deep dive into the importance of fairness in ML decision-making.

Examining fairness in recommender systems and the impact of biases.

New method optimizes hyperparameters by balancing multiple goals in machine learning.

Addressing bias in AI models to improve patient care in radiology.

Discover new methods for fairly dividing resources among individuals and groups.

Understanding the key factors in client selection for improved federated learning.

A study on using triplet loss to create fairer machine learning models.

Content moderation is crucial for the responsible use of generative AI systems.

A fresh approach to reduce bias in facial expression recognition systems.

A method to reduce bias in AI training datasets for fairer outcomes.

A new approach enhances fairness and safety in ML systems.

This article presents a new method for ordering transactions fairly and efficiently.

An overview of federated learning's privacy and fairness challenges.

A new approach improves mutual recommendations in job and dating platforms.

Introducing GMMD, a framework to enhance fairness in graph neural networks.

Examining bias and fairness measures in biometric technology performance.

Examining fairness across hiring, healthcare, lending, and renting using simulated data logs.

This paper discusses the importance of fairness in automated decision-making systems.

Examining the balance between privacy and fairness in federated learning models.

Examining the interplay of fairness, privacy, and predictive performance in machine learning.

Addressing fairness in machine learning models across multiple tasks.

A study on the healthcare challenges faced by nursing home residents in the Basque Country during the pandemic.

Using UDA methods enhances skin lesion classification and promotes fairness across demographics.

A new framework allows tailored synthetic data generation to meet varied organizational needs.

This study focuses on creating equitable dialogue systems through machine learning insights.

Examining how data order impacts fairness in machine learning models.

A new method to improve fairness in federated learning models while maintaining privacy.

DBFed aims to reduce bias in AI while maintaining data privacy.

A new approach improves fairness in reinforcement learning using human feedback.

Analyzing fairness in recommendation systems using counterfactual explanations.

Small biases in AI can lead to major unfair outcomes.