Latest Articles for Fairness

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.

Federated learning offers a way to utilize data while maintaining user privacy.

Study reveals how players quickly assess new games' fun factor.

Examining AI decision-making and its unexpected challenges.

Evaluating machine learning models to ensure fairness across diverse populations.

A framework for ethical AI development in healthcare systems.

Examining how race affects healthcare models and their fairness.

Selective debiasing aims to improve fairness in machine learning predictions.

CrossWalk method improves fairness in node embeddings while considering sensitive information.

Exploring how AI can improve learning, assessment, and fairness.

New models improve content moderation for AI interactions, ensuring safety and reducing harm.

Exploring methods to fairly allocate shared resources among community members.

Exploring how randomness can improve fairness in resource distribution.

A new approach for resource allocation in social programs prioritizes needs and evaluation.

A look at bias and fairness in computer vision technology.

Examining cultural differences in depression prediction models and fairness challenges.

This article examines fairness in auction design and its impact on resource allocation.

Examining the challenges of interpreting linear regression models in machine learning.

Shuffling attacks reveal vulnerabilities in AI fairness assessments using methods like SHAP.

New methods enhance face recognition while addressing privacy concerns.

Introducing BMFT: a method to enhance fairness in machine learning without original training data.

Examining how example choices affect fairness in language models.

A new version of the Ultimatum Game reveals complex dynamics of fairness in group settings.

A new method aims to make high school scheduling fairer for all students.

A new approach to reduce bias in artificial intelligence systems.

A look at reducing bias in AI-generated images.

A look at the key differences between Explainable AI and Interpretable AI.

A guide to ethical requirements for developing responsible AI systems.

Exploring fairness in personalized recommendations using graph neural networks.

Discover how bias in ML affects public health outcomes and fairness.

Exploring new methods for fair decision-making in machine learning systems.

Examining the importance of fairness in AI systems impacting lives.

A new method aims to enhance fairness in machine learning through data sample reweighting.

A look at evaluating trustworthy AI systems and the methods involved.

Examining bias in foundation models and their impact on medical imaging outcomes.

This study explores fairness in chest X-ray analysis without demographic details.

Examining the principles and challenges of creating fair AI technologies.

A study on Graphair's effectiveness in fair graph representations and link prediction.