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 …

Adolescents spend a significant amount of time on social media and there is a great public
worry, from parents to policy makers, about the effect of social media on healthy …

We investigate how annotators' insensitivity to differences in dialect can lead to racial bias in
automatic hate speech detection models, potentially amplifying harm against minority …

Despite inextricable ties between race and language, little work has considered race in NLP
research and development. In this work, we survey 79 papers from the ACL anthology that …

Biased associations have been a challenge in the development of classifiers for detecting
toxic language, hindering both fairness and accuracy. As potential solutions, we investigate …

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

With language models becoming increasingly ubiquitous, it has become essential to
address their inequitable treatment of diverse demographic groups and factors. Most …

Existing research on fairness evaluation of document classification models mainly uses
synthetic monolingual data without ground truth for author demographic attributes. In this …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Language usage on social media varies widely even within the context of American English.
Despite this, the majority of natural language processing systems are trained only on …