3 papers
cs.LG2026
Interpretability Can Be Actionable
Hadas Orgad, Fazl Barez, Tal Haklay +9
Interpretability aims to explain the behavior of deep neural networks. Despite rapid growth, there is mounting concern that much of this work has not translated into practical impa…
cs.AI2025
Large language models require a new form of oversight: capability-based monitoring
Katherine C. Kellogg, Bingyang Ye, Yifan Hu +3
The rapid adoption of large language models (LLMs) in healthcare has been accompanied by scrutiny of their oversight. Existing monitoring approaches, inherited from traditional mac…
cs.LG2025
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…