Building Understandable Messaging for Policy and Evidence Review (BUMPER) with AI

التفاصيل البيبلوغرافية
العنوان: Building Understandable Messaging for Policy and Evidence Review (BUMPER) with AI
المؤلفون: Rosenfeld, Katherine A., Sonnewald, Maike, Jindal, Sonia J., McCarthy, Kevin A., Proctor, Joshua L.
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
الوصف: We introduce a framework for the use of large language models (LLMs) in Building Understandable Messaging for Policy and Evidence Review (BUMPER). LLMs are proving capable of providing interfaces for understanding and synthesizing large databases of diverse media. This presents an exciting opportunity to supercharge the translation of scientific evidence into policy and action, thereby improving livelihoods around the world. However, these models also pose challenges related to access, trust-worthiness, and accountability. The BUMPER framework is built atop a scientific knowledge base (e.g., documentation, code, survey data) by the same scientists (e.g., individual contributor, lab, consortium). We focus on a solution that builds trustworthiness through transparency, scope-limiting, explicit-checks, and uncertainty measures. LLMs are rapidly being adopted and consequences are poorly understood. The framework addresses open questions regarding the reliability of LLMs and their use in high-stakes applications. We provide a worked example in health policy for a model designed to inform measles control programs. We argue that this framework can facilitate accessibility of and confidence in scientific evidence for policymakers, drive a focus on policy-relevance and translatability for researchers, and ultimately increase and accelerate the impact of scientific knowledge used for policy decisions.
Comment: 21 pages, 6 figures
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2407.12812
رقم الأكسشن: edsarx.2407.12812
قاعدة البيانات: arXiv