2 citations · 2 across the 4 of their papers we have counts for
4 papers
What Do People Actually Want From AI? Mapping Preference Plurality
Julia Sepúlveda Coelho, Scott A. Hale
Large Language Models (LLMs) are often fine-tuned through Reinforcement Learning from Human Feedback (RLHF) to align with people's preferences and values. However, this method has…
Evaluating AI-based Scientific Knowledge Synthesis with Epidemiological Systematic Reviews
Shreyansh Padarha, Ryan Othniel Kearns, Tristan Naidoo +13
Systematic literature reviews (SLRs) are a demanding and high-stakes form of scientific knowledge synthesis that remains underspecified as an evaluation setting for large language…
AI-Powered Detection of Inappropriate Language in Medical School Curricula
Chiman Salavati, Shannon Song, Scott A. Hale +3
The use of inappropriate language -- such as outdated, exclusionary, or non-patient-centered terms -- medical instructional materials can significantly influence clinical training,…
Reducing Biases towards Minoritized Populations in Medical Curricular Content via Artificial Intelligence for Fairer Health Outcomes
Chiman Salavati, Shannon Song, Willmar Sosa Diaz +4
Biased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce BRICC, a firstin…