568 citations · 582 across the 15 of their papers we have counts for
16 papers
Same Chart, Different Story: Bias in Vision-Language Chart Interpretation
Mizanur Rahman, Huan Wu, Arash Asgari +2
Vision-language models (VLMs) are increasingly used to interpret charts and generate natural-language explanations for socially consequential data. However, they may produce differ…
LLMs Silently Correct African American English: Auditing and Mitigating Dialect Bias via Activation Steering
Huan Wu, Ali Emami, Muhammad Furquan Hassan +5
African American English (AAE), a rule-governed dialect spoken by over 30 million people, is routinely misinterpreted and "corrected" by large language models (LLMs). Across six in…
Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support
Mizanur Rahman, Abeer Badawi, Elahe Rahimi +4
Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive…
Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour
Abeer Badawi, Moyosoreoluwa Olatosi, Negin Baghbanzadeh +5
Recent incidents involving LLMs used for mental-health support reveal a critical evaluation gap: surface-level safety scores do not capture how models behave across realistic, emot…
Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types
Chi-Yu Chen, Rawan Abulibdeh, Arash Asgari +8
Artificial intelligence is revealing what medicine never intended to encode. Deep vision models, trained on chest X-rays, can now detect not only disease but also invisible traces…
We Politely Insist: Your LLM Must Learn the Persian Art of Taarof
Nikta Gohari Sadr, Sahar Heidariasl, Karine Megerdoomian +2
Large language models (LLMs) struggle to navigate culturally specific communication norms, limiting their effectiveness in global contexts. We focus on Persian taarof, a social nor…
Template-Based Probes Are Imperfect Lenses for Counterfactual Bias Evaluation in LLMs
Farnaz Kohankhaki, D. B. Emerson, Jacob-Junqi Tian +2
Bias in large language models (LLMs) has many forms, from overt discrimination to implicit stereotypes. Counterfactual bias evaluation is a widely used approach to quantifying bias…
Benchmarking bias: Expanding clinical AI model card to incorporate bias reporting of social and non-social factors
Carolina A. M. Heming, Mohamed Abdalla, Shahram Mohanna +8
Clinical AI model reporting cards should be expanded to incorporate a broad bias reporting of both social and non-social factors. Non-social factors consider the role of other fact…
Towards Trustworthy Artificial Intelligence for Equitable Global Health
Hong Qin, Jude Kong, Wandi Ding +14
Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional de…
Soft-prompt Tuning for Large Language Models to Evaluate Bias
Jacob-Junqi Tian, David Emerson, Sevil Zanjani Miyandoab +3
Prompting large language models has gained immense popularity in recent years due to the advantage of producing good results even without the need for labelled data. However, this…
MLHOps: Machine Learning for Healthcare Operations
Faiza Khan Khattak, Vallijah Subasri, Amrit Krishnan +4
Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in he…
Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography
Linglin Zhang, Beatrice Brown-Mulry, Vineela Nalla +7
Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with incre…
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns +20
Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obviou…
An Empirical Framework for Domain Generalization in Clinical Settings
Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari +3
Clinical machine learning models experience significantly degraded performance in datasets not seen during training, e.g., new hospitals or populations. Recent developments in doma…
Evaluating Knowledge Transfer in Neural Network for Medical Images
Sina Akbarian, Laleh Seyyed-Kalantari, Farzad Khalvati +1
Deep learning and knowledge transfer techniques have permeated the field of medical imaging and are considered as key approaches for revolutionizing diagnostic imaging practices. H…
CheXclusion: Fairness gaps in deep chest X-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott +2
Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we ex…