Latest Articles for Diversity And Inclusion

Highlighting the need for fairness in mental health speech datasets.

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

Evaluating risks of biased outcomes in robots using language models.

Examining how our beliefs about others impact mental health and relationships.

JobFair framework identifies gender bias in automated resume evaluations.

Study shows transport links can reduce segregation between native and foreign-born groups.

This study explores the trade-off between diversity and factual accuracy in AI-generated images.

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

Examining the impact of LLMs on social stereotyping and ways to improve outcomes.

Examining biases in AI depictions of Indian culture.

A comprehensive study of 15 years in algorithmic fairness research reveals key trends.

This article examines diverse experiences in quantum education programs in Colorado.

Examining the FairJob dataset to ensure equal job opportunities for all.

A new method improves heart rate measurement for diverse skin tones.

A look at reducing bias in AI-generated images.

Examining AI's role in equity during hiring processes.

A study reveals significant racial bias in emotion recognition technologies.

New tools assess how diverse cultures adapt in changing environments.

This study explores how synthetic data can tackle class and group imbalances.

Examining how biases in LLMs affect software team recruitment.

This paper discusses fairness in selecting candidates for institutions amid biased evaluations.

Analyzing how gender-fair language influences language models in text classification tasks.

WiCV 2024 showcased women's contributions in computer vision through talks and networking.

Methods to create fair summaries from diverse social media voices.

A new method enhances how AI handles pronouns for better representation.

This article discusses the importance of diversity in AI-generated images.

New methods tackle fairness in dense subgraph discovery.

Addressing cultural biases in multilingual evaluation for better language model performance.

Enhancing clinical trials for better representation and real-world relevance.

Research reveals pros and cons of Mixup techniques for fairness in AI.

This article highlights the need for diverse representation in software engineering education.

A new method aligns language models with diverse group preferences.

Ensuring equity in AI systems is essential for responsible technology deployment.