Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

Abstract Machine learning algorithms are widely used in management systems in different
fields, such as employee recruitment, loan provision, disease diagnosis, etc., and even in …

Bias detection in text is crucial for combating the spread of negative stereotypes,
misinformation, and biased decision-making. Traditional language models frequently face …

Since they started to recognise the potential to cut costs, boost productivity, and achieve
greater levels of quality, many firms have been adjusting their operations to a process …

While there has been considerable amount of research on bias mitigation algorithms, two
properties: multi-community perspective and fairness to* all* communities have not been …

While the accuracy-fairness trade-off has been frequently observed in the literature of fair
machine learning, rigorous theoretical analyses have been scarce. To demystify this long …

In the digital information age, detecting and mitigating linguistic biases, particularly in
political discourse, presents a critical challenge. This study addresses this issue by …

Le développement massif des technologies en traitement automatique du langage naturel
(TALN), ainsi que l'obsession grandissante de la taille des modèles de langage utilisés, s' …

Abstract Natural Language Processing (NLP) systems are included everywhere on the
internet from search engines, language translations to more advanced systems like voice …

The field of artificial intelligence and data science known as natural language processing
(NLP) is expanding rapidly, and it makes use of sophisticated speech and text processing …