Hate speech is one type of harmful online content which directly attacks or promotes hate
towards a group or an individual member based on their actual or perceived aspects of …

Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors
such as the context, geography, socio-political climate, and background of the producers …

Without proper safeguards, large language models will readily follow malicious instructions
and generate toxic content. This risk motivates safety efforts such as red-teaming and large …

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 …

Little attention is placed on analyzing nationality bias in language models, especially when
nationality is highly used as a factor in increasing the performance of social NLP models …

Detecting online hate is a difficult task that even state-of-the-art models struggle with.
Typically, hate speech detection models are evaluated by measuring their performance on …

We present a human-and-model-in-the-loop process for dynamically generating datasets
and training better performing and more robust hate detection models. We provide a new …

Abstract Language models have revolutionized the field of NLP. However, language models
capture and proliferate hurtful stereotypes, especially in text generation. Our results show …

Social stereotypes negatively impact individuals' judgments about different groups and may
have a critical role in understanding language directed toward marginalized groups. Here …

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 …