Latest Articles for Fairness

This article discusses fairness measurement in recommendation systems and proposes sampling methods.

Analyzing prediction accuracy across different communities for equitable health resource allocation.

Research shows how demographics shape fairness views in AI content moderation.

This paper discusses methods to ensure fairness in AI through self-supervised learning techniques.

A new framework tackles fairness conflicts in machine learning effectively.

Testing LLMs is essential for safe and effective AI applications.

This article investigates gender bias in language models used for depression evaluation.

EquiPrompt aims to reduce biases in AI-generated images using innovative methods.

Examining the impact of dialects on language bias detection accuracy.

New methods enhance prediction accuracy and fairness in healthcare outcomes.

Researchers develop GECO dataset and GECOBench to tackle gender bias in AI.

Recommender systems influence user experiences but face key concerns regarding fairness and privacy.

This paper explores fairness and stability in machine learning models affected by their predictions.

Addressing biases in face recognition through balanced training datasets.

Data balancing is crucial for fairness and accuracy in machine learning models.

Exploring fairness issues in AI language models and their implications.

Examining the need for fairness in AI and its impact on society.

Tools like OxonFair help ensure fairness in AI decision-making.

A framework to reduce bias in AI language models while maintaining accuracy.

Examining how student relationships affect peer grading accuracy.

Introducing FairMedFM to evaluate the fairness of foundation models in healthcare.

Learn how information travels through networks based on specific rules and symbols.

A new framework tackles fairness in deep neural networks during model pruning.

This study examines secure communication strategies in multi-UAV systems.

The Rashomon Effect reveals multiple effective models in machine learning.

A look into fair resource distribution in housing.

Examining the role of dropout techniques in improving fairness in DNNs.

A new framework improves AI medical image classification by addressing biases and forgetting outdated information.

This study addresses fairness in medical imaging through synthetic data and new methods.

Regulations guide the safe and fair use of AI technologies across various sectors.

RSMA improves data fairness and performance in multi-group multicast scenarios.

Study reveals importance of clear instructions for AI in clinical settings.

This article discusses fair and efficient ways to allocate indivisible goods.

A new framework addresses fairness, privacy, interpretability, and emissions in AI.

This study investigates fair sharing of indivisible items to reduce envy and encourage honesty.

This study analyzes how platforms promote their own products in search results.

PUFFLE offers a solution for privacy, utility, and fairness challenges in machine learning.

Learn how new systems improve communication reliability and user satisfaction.

Examining the impact and challenges of Responsible AI research in real-world applications.

Examining the challenges of fairness in AI for medical imaging.