Text classification in natural language processing (NLP) is evolving rapidly, particularly with
the surge in transformer-based models, including large language models (LLM). This paper …

Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …

Recently, there has been an increased interest in demographically grounded bias in natural
language processing (NLP) applications. Much of the recent work has focused on describing …

We survey 146 papers analyzing" bias" in NLP systems, finding that their motivations are
often vague, inconsistent, and lacking in normative reasoning, despite the fact that …

Pre-trained models have revolutionized natural language understanding. However,
researchers have found they can encode artifacts undesired in many applications, such as …

Language can be used as a means of reproducing and enforcing harmful stereotypes and
biases and has been analysed as such in numerous research. In this paper, we present a …

Measuring bias is key for better understanding and addressing unfairness in NLP/ML
models. This is often done via fairness metrics, which quantify the differences in a model's …

There has been a recent wave of work assessing the fairness of machine learning models in
general, and more specifically, on natural language processing (NLP) models built using …

The rise of concern around Natural Language Processing (NLP) technologies containing
and perpetuating social biases has led to a rich and rapidly growing area of research …

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or
Machine Learning (ML) field is undergoing rapid growth concerning research and real-world …