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 …

We survey 146 papers analyzing" bias" in NLP systems, finding that their motivations are
often vague, inconsistent, and lacking in normative reasoning, despite the fact that …

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 …

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' …

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 …

Measuring bias is key for better understanding and addressing unfairness in NLP/ML
models. This is often done via fairness metrics, which quantify the differences in a model's …

There has been a recent wave of work assessing the fairness of machine learning models in
general, and more specifically, on natural language processing (NLP) models built using …

Abstract Analysis of subjective texts like offensive content or hate speech is a great
challenge, especially regarding annotation process. Most of current annotation procedures …

Hate speech detection is a challenging problem with most of the datasets available in only
one language: English. In this paper, we conduct a large scale analysis of multilingual hate …