25 citations · 55 across the 12 of their papers we have counts for
13 papers
Adapting Psycholinguistic Research for LLMs: Gender-inclusive Language in a Coreference Context
Marion Bartl, Thomas Brendan Murphy, Susan Leavy
Gender-inclusive language is often used with the aim of ensuring that all individuals, regardless of gender, can be associated with certain concepts. While psycholinguistic studies…
Generated Bias: Auditing Internal Bias Dynamics of Text-To-Image Generative Models
Abhishek Mandal, Susan Leavy, Suzanne Little
Text-To-Image (TTI) Diffusion Models such as DALL-E and Stable Diffusion are capable of generating images from text prompts. However, they have been shown to perpetuate gender ster…
From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs
Marion Bartl, Susan Leavy
Gender bias is not only prevalent in Large Language Models (LLMs) and their training data, but also firmly ingrained into the structural aspects of language itself. Therefore, adap…
Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?
Abhishek Mandal, Susan Leavy, Suzanne Little
Deep neural networks used in computer vision have been shown to exhibit many social biases such as gender bias. Vision Transformers (ViTs) have become increasingly popular in compu…
Gender Bias in Multimodal Models: A Transnational Feminist Approach Considering Geographical Region and Culture
Abhishek Mandal, Suzanne Little, Susan Leavy
Deep learning based visual-linguistic multimodal models such as Contrastive Language Image Pre-training (CLIP) have become increasingly popular recently and are used within text-to…
Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning
Susan Leavy, Emilie Pine, Mark T Keane
We present a text mining system to support the exploration of large volumes of text detailing the findings of government inquiries. Despite their historical significance and potent…
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts
Susan Leavy, Gerardine Meaney, Karen Wade +1
The increasing availability of digital collections of historical and contemporary literature presents a wealth of possibilities for new research in the humanities. The scale and di…
Multimodal Composite Association Score: Measuring Gender Bias in Generative Multimodal Models
Abhishek Mandal, Susan Leavy, Suzanne Little
Generative multimodal models based on diffusion models have seen tremendous growth and advances in recent years. Models such as DALL-E and Stable Diffusion have become increasingly…
Inclusive Ethical Design for Recommender Systems
Susan Leavy
Recommender systems are becoming increasingly central as mediators of information with the potential to profoundly influence societal opinion. While approaches are being developed…
Towards Lexical Gender Inference: A Scalable Methodology using Online Databases
Marion Bartl, Susan Leavy
This paper presents a new method for automatically detecting words with lexical gender in large-scale language datasets. Currently, the evaluation of gender bias in natural languag…
Data, Power and Bias in Artificial Intelligence
Susan Leavy, Barry O'Sullivan, Eugenia Siapera
Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. Data used to train machine learning algori…
Mitigating Gender Bias in Machine Learning Data Sets
Susan Leavy, Gerardine Meaney, Karen Wade +1
Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the co…
Uncovering Gender Bias in Media Coverage of Politicians with Machine Learning
Susan Leavy
This paper presents research uncovering systematic gender bias in the representation of political leaders in the media, using artificial intelligence. Newspaper coverage of Irish m…