The rapid progress in Large Language Models (LLMs) could transform many fields, but their
fast development creates significant challenges for oversight, ethical creation, and building …

Bias in textual data can lead to skewed interpretations and outcomes when the data is used.
These biases could perpetuate stereotypes, discrimination, or other forms of unfair …

Generative AI technologies, particularly Large Language Models (LLMs), have transformed
information management systems but introduced substantial biases that can compromise …

Large Language models (LLMs), while powerful, exhibit harmful social biases. Debiasing is
often challenging due to computational costs, data constraints, and potential degradation of …

This paper introduces the concept of actionability in the context of bias measures in natural
language processing (NLP). We define actionability as the degree to which a …

Group fairness is a central research topic in text classification, where reaching fair treatment
between sensitive groups (eg women vs. men) remains an open challenge. This paper …

In this study, we investigate gender bias in Bangla pretrained language models, a largely
under explored area in low-resource languages. To assess this bias, we applied gender …

Introduction: Healthcare AI models often inherit biases from their training data. While efforts
have primarily targeted bias in structured data, mental health heavily depends on …

With the introduction of (large) language models, there has been significant concern about
the unintended bias such models may inherit from their training data. A number of studies …

Navigating the uncertainties of job classification and gender bias, this paper presents multi-
objective learning approach using BERT-based model that concurrently handles maximizing …