Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

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 …

In this paper, we study counterfactual fairness in text classification, which asks the question:
How would the prediction change if the sensitive attribute referenced in the example were …

Abstract Machine Learning has seen tremendous growth recently, which has led to a larger
adaptation of ML systems for educational assessments, credit risk, healthcare, employment …

Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the
drawbacks of group fairness. Despite its benefits, it depends on a task specific fair metric that …

Machine learning algorithms are becoming ubiquitous in modern life. When used to help
inform human decision making, they have been criticized by some for insufficient accuracy …

With the introduction of ChatGPT, OpenAI made large language models (LLM) accessible to
users with limited IT expertise. However, users with no background in natural language …

In this paper, we focus on the detection of sexist hate speech against women in tweets
studying for the first time the impact of gender stereotype detection on sexism classification …

Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these
language technologies do not accrue evenly to all users, and indeed they can be harmful; …