Latest Articles for 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.

Exploring fairness in online dating and job recommendation systems.

An analysis of fairness testing tools for software developers.

Examining fairness issues in transaction fraud detection systems.

This study analyzes the fairness of diffusion-based recommendation methods compared to traditional models.

This article discusses fair allocation methods for tasks and jobs using maximin shares.

Exposing the manipulation risks of influence functions in machine learning.

This article discusses fair allocation of items using machine learning techniques.

Research on improving fairness in charitable donations through better tools.

Study reveals how timing and beneficiary influence fairness perceptions during robot interactions.