12 citations · 18 across the 6 of their papers we have counts for
6 papers
Articulation Work and Tinkering for Fairness in Machine Learning
Miriam Fahimi, Mayra Russo, Kristen M. Scott +3
The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and redu…
Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptation
François Remy, Pieter Delobelle, Bettina Berendt +2
Training monolingual language models for low and mid-resource languages is made challenging by limited and often inadequate pretraining data. In this study, we propose a novel mode…
Bias, diversity, and challenges to fairness in classification and automated text analysis. From libraries to AI and back
Bettina Berendt, Özgür Karadeniz, Sercan Kıyak +2
Libraries are increasingly relying on computational methods, including methods from Artificial Intelligence (AI). This increasing usage raises concerns about the risks of AI that a…
How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification
Ewoenam Tokpo, Pieter Delobelle, Bettina Berendt +1
To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use o…
FairDistillation: Mitigating Stereotyping in Language Models
Pieter Delobelle, Bettina Berendt
Large pre-trained language models are successfully being used in a variety of tasks, across many languages. With this ever-increasing usage, the risk of harmful side effects also r…
Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models
Pieter Delobelle, Ewoenam Kwaku Tokpo, Toon Calders +1
An increasing awareness of biased patterns in natural language processing resources, like BERT, has motivated many metrics to quantify `bias' and `fairness'. But comparing the resu…