Search Results for author:
Found 9 papers, 3 papers with code
Inferring Gender: A Scalable Methodology for Gender Detection with Online Lexical Databases
This paper presents a new method for automatic detection of gendered terms in large-scale language datasets.
Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?
We examine bias amplification when models belonging to these two architectures are used as a part of large multimodal models, evaluating the different image encoders of Contrastive Language Image Pretraining which is an important model used in many generative models such as DALL-E and Stable Diffusion.
Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning
We present a text mining system to support the exploration of large volumes of text detailing the findings of government inquiries.
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts
The increasing availability of digital collections of historical and contemporary literature presents a wealth of possibilities for new research in the humanities.
Multimodal Composite Association Score: Measuring Gender Bias in Generative Multimodal Models
In this paper, we propose Multimodal Composite Association Score (MCAS) as a new method of measuring gender bias in multimodal generative models.
Inclusive Ethical Design for Recommender Systems
Recommender systems are becoming increasingly central as mediators of information with the potential to profoundly influence societal opinion.
Towards Lexical Gender Inference: A Scalable Methodology using Online Databases
and nouns with lexical gender ('mother', 'boyfriend', 'policewoman', etc.).
Uncovering Gender Bias in Media Coverage of Politicians with Machine Learning
This paper presents research uncovering systematic gender bias in the representation of political leaders in the media, using artificial intelligence.
Mitigating Gender Bias in Machine Learning Data Sets
Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society.
