Latest Articles for Data Ethics

A new framework aims to ensure equity in GNN predictions.

Examining fairness challenges in GNNs amidst changing data distributions.

This article examines geographic bias in LLMs and its societal impacts.

This article explores the use of AI in predicting crime locations and its implications.

This article investigates gender and racial biases in large vision-language models.

This research focuses on reducing multiple biases in language models simultaneously.

A study on identifying and reducing cognitive bias in large language models.

A study on students' views regarding fairness and ethics in MMLA.

A look at how models can perpetuate bias and impact fairness.

A novel method for fair data collection in predictive modeling.

Examining methods to assess fairness in clinical decision support algorithms.

A dataset to evaluate social biases in Indian language models.

This article discusses robust accurate fairness in deep learning and its effects.

Research highlights the importance of fairness in machine learning language representations.

Exploring methods to achieve fairness in classification systems without sacrificing performance.

A new model enhances data fusion and analysis in healthcare.

This paper introduces FairGI, a framework for fairness in graph neural networks.

A framework for evaluating fairness in recommendation systems influenced by user attributes.

Exploring how absence can inform better algorithmic design and promote equity.

New benchmark assesses toxicity in large language models across various languages.

This article discusses the importance of fairness in machine learning predictions.

Examining the importance of data valuation for language models and its implications.

This article discusses the importance of data practices for fairness in machine learning.

A practical method to improve fairness in algorithms without sacrificing accuracy.

This article examines if language models possess beliefs and follow coherence norms.

Highlighting the need for fairness in mental health speech datasets.

Fair datasets are crucial for unbiased machine learning models.

Examining the risks and safety measures in fine-tuning language models.

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

This study presents a new method for identifying key training images in AI-generated visuals.

Exploring techniques for reducing bias in advanced language models.

A new method tackles hidden threats in large language models.

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

This article discusses methods for improving AI alignment with various cultures.

Assessing strategies to manage copyright issues in language models.

This workshop analyzes gender stereotypes in AI through the lens of societal biases.

Improving how machines answer visual questions through structured reasoning.

A look at methods to reduce bias in automated decisions using Fair Representation Learning.

This paper discusses methods to reduce bias in AI image and text datasets.

FairPFN uses transformers to promote fairness in machine learning predictions.