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 …

Machine learning models are commonly used to detect toxicity in online conversations.
These models are trained on datasets annotated by human raters. We explore how raters' …

Abstract Machine learning is recently used to detect hate speech and other forms of abusive
language in online platforms. However, a notable weakness of machine learning models is …

Annotators' disagreement in linguistic data has been recently the focus of multiple initiatives
aimed at raising awareness on issues related to 'majority voting'when aggregating diverging …

Automatic detection of abusive online content such as hate speech, offensive language,
threats, etc. has become prevalent in social media, with multiple efforts dedicated to …

Motivations for methods in explainable artificial intelligence (XAI) often include detecting,
quantifying and mitigating bias, and contributing to making machine learning models fairer …

There is an enormous growth of social media which fully promotes freedom of expression
through its anonymity feature. Freedom of expression is a human right but hate speech …

Online misogyny, a category of online abusive language, has serious and harmful social
consequences. Automatic detection of misogynistic language online, while imperative …

Warning: This paper contains content that is offensive and may be upsetting. Biased or toxic
speech can be harmful to various demographic groups. Therefore, it is not only important for …

Language models have shown promise in various tasks but can be affected by undesired
data during training, fine-tuning, or alignment. For example, if some unsafe conversations …