6 papers
FairJudge: Abstention-Aware Multimodal Judges for Fairness and Alignment Evaluation in Text-to-Image Models
Zahraa Al Sahili, Maimuna Nowaz, Maryam Fetanat +2
Evaluating text-to-image (T2I) systems requires judging not only whether an image matches a prompt, but also whether socially salient attributes are represented faithfully and with…
Data Matters Most: Auditing Social Bias in Contrastive Vision Language Models
Zahraa Al Sahili, Ioannis Patras, Matthew Purver
Vision-language models (VLMs) deliver strong zero-shot recognition but frequently inherit social biases from their training data. We systematically disentangle three design factors…
Breaking Language Barriers or Reinforcing Bias? A Study of Gender and Racial Disparities in Multilingual Contrastive Vision Language Models
Zahraa Al Sahili, Ioannis Patras, Matthew Purver
Multilingual vision-language models (VLMs) promise universal image-text retrieval, yet their social biases remain underexplored. We perform the first systematic audit of four publi…
Towards deployment-centric multimodal AI beyond vision and language
Xianyuan Liu, Jiayang Zhang, Shuo Zhou +45
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as h…
FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models
Zahraa Al Sahili, Ioannis Patras, Matthew Purver
In the domain of text-to-image generative models, biases inherent in training datasets often propagate into generated content, posing significant ethical challenges, particularly i…
Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges
Zahraa Al Sahili, Ioannis Patras, Matthew Purver
Multimodal machine learning (MML) is rapidly reshaping the way mental-health disorders are detected, characterized, and longitudinally monitored. Whereas early studies relied on is…