Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI)
algorithms present promising solutions to a variety of technological and non-technological …

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 …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Text representation models are prone to exhibit a range of societal biases, reflecting the non-
controlled and biased nature of the underlying pretraining data, which consequently leads to …

BERT and other large-scale language models (LMs) contain gender and racial bias. They
also exhibit other dimensions of social bias, most of which have not been studied in depth …

Warning: This paper contains examples of gender non-affirmative language which could be
offensive, upsetting, and/or triggering. Transgender and non-binary (TGNB) individuals …

Dialogue models trained on human conversations inadvertently learn to generate toxic
responses. In addition to producing explicitly offensive utterances, these models can also …

Powered by advanced Artificial Intelligence (AI) techniques, conversational AI systems, such
as ChatGPT, and digital assistants like Siri, have been widely deployed in daily life …

Recent studies show that Natural Language Processing (NLP) technologies propagate
societal biases about demographic groups associated with attributes such as gender, race …