Latest Articles for Bias

Researchers use propositional probes to enhance the reliability of language models.

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

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

A study on bias in synthetic image generation models and their societal impacts.

PopAlign aims to reduce bias in text-to-image generation while maintaining quality.

Exploring the challenges and solutions for bias in machine learning.

A novel method enhances the accuracy of treatment effect estimates over time.

Examining the relationship between privacy techniques and biases in language models.

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

Names from different countries impact how classifiers interpret social media content.

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

Exploring how synthetic data shapes machine learning models and their behavior.

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

A new framework analyzes and reduces bias in vision-language models through targeted interventions.

Analyzing genetic traits through advanced covariance matrix techniques.

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

Investigating the effects of polarized foregrounds on CMB measurements.

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

Learn how importance weighting enhances model performance amid covariate shifts.

Exploring how too many options can complicate decision-making.

An investigation into advocacy influence in medical journal publications during COVID-19.

Exploring the risks and implications of replication in visual diffusion model technology.

Study reveals gender stereotypes in emotional responses of Bangla language models.

This study reveals LLMs' preference for their own output over human-written content.

A study on how stereotypes affect language models using the GlobalBias dataset.

Bayes factors help researchers evaluate and compare hypotheses using data-driven evidence.

A new method improves multi-task learning by aggregating targets and features.

Exploring how machine learning techniques assist in solving inverse problems across various fields.

A critical look at diversity in datasets and how to improve it.

A study on multilingual Facebook posts related to the Israeli War on Gaza.

BiasAlert enhances bias detection in language models for fairer AI outputs.

This article discusses using Reinforcement Learning to reduce bias in classification tasks.

Research presents a framework to reduce bias in AI-generated text.

Examining the need for diversity and inclusion in AI development.

Using external controls can enhance treatment effect estimates in clinical trials.

A robust dataset for training advanced chat-based AI systems.

A new method improves fairness in healthcare data modeling.

Explore various methods for estimating entropy in data analysis.

Understanding how shorter planning horizons improve decision-making with limited information.

A new dataset aims to improve facial recognition accuracy for diverse populations.