2 citations · 2 across the 7 of their papers we have counts for
11 papers
Balancing Safety and Helpfulness in Healthcare AI Assistants through Iterative Preference Alignment
Huy Nghiem, Swetasudha Panda, Devashish Khatwani +3
Large Language Models (LLMs) are increasingly used in healthcare, yet ensuring their safety and trustworthiness remains a barrier to deployment. Conversational medical assistants m…
'Rich Dad, Poor Lad': How do Large Language Models Contextualize Socioeconomic Factors in College Admission ?
Huy Nghiem, Phuong-Anh Nguyen-Le, John Prindle +2
Large Language Models (LLMs) are increasingly involved in high-stakes domains, yet how they reason about socially sensitive decisions remains underexplored. We present a large-scal…
Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users
Jeffri Murrugarra-LLerena, Haoran Niu, K. Suzanne Barber +3
As visual assistant systems powered by visual language models (VLMs) become more prevalent, concerns over user privacy have grown, particularly for blind and low vision users who m…
An Interdisciplinary Approach to Human-Centered Machine Translation
Marine Carpuat, Omri Asscher, Kalika Bali +17
Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between sy…
How May U.S. Courts Scrutinize Their Recidivism Risk Assessment Tools? Contextualizing AI Fairness Criteria on a Judicial Scrutiny-based Framework
Tin Nguyen, Jiannan Xu, Phuong-Anh Nguyen-Le +4
The AI/HCI and legal communities have developed largely independent conceptualizations of fairness. This conceptual difference hinders the potential incorporation of technical fair…
Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics
Tin Trung Nguyen, Jiannan Xu, Zora Che +6
Although popularized AI fairness metrics, e.g., demographic parity, have uncovered bias in AI-assisted decision-making outcomes, they do not consider how much effort one has spent…