Text-to-image generative models often reflect the biases of the training data, leading to
unequal representations of underrepresented groups. This study investigates inclusive text …

Computer vision models have known performance disparities across attributes such as
gender and skin tone. This means during tasks such as classification and detection, model …

The increasing tendency to collect large and uncurated datasets to train vision-and-
language models has raised concerns about fair representations. It is known that even small …

Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of
applications. In this paper, a thorough examination of the research landscape in GAI is …

This paper introduces a new large consent-driven dataset aimed at assisting in the
evaluation of algorithmic bias and robustness of computer vision and audio speech models …

Speech datasets are crucial for training Speech Language Technologies (SLT); however,
the lack of diversity of the underlying training data can lead to serious limitations in building …

Machine learning (ML) datasets, often perceived as neutral, inherently encapsulate abstract
and disputed social constructs. Dataset curators frequently employ value-laden terms such …

The race to develop image generation models is intensifying, with a rapid increase in the
number of text-to-image models available. This is coupled with growing public awareness of …

Stress impacts our physical and mental health as well as our social life. A passive and
contactless indoor stress monitoring system can unlock numerous important applications …

In an era where data profoundly influences decision-making across various sectors, this
comprehensive review critically examines the evolving landscape of data science ethics …