NRT-AI: AI Advancements and Convergence in Computational, Environmental, and Social Sciences (AI-ACCESS)

新兴的人工智能和计算工具有可能带来科学实践的重大变革，特别是在环境和社会科学领域。事实上，随着社会系统越来越多地嵌入到调解日常人类活动的计算平台中，这些领域中重要问题的性质本身正在发生变化。然而，尽管巨大的数据集在环境和社会科学中迅速变得司空见惯，但理解人类行为产生的数据的正确方法，以及研究这些数据的可用工具，都是缺乏的。为响应这一迫切需求，国家科学基金会研究培训(NRT)奖与休斯顿大学市中心分校(UHD)合作，在圣路易斯华盛顿大学(WASHU)设立了计算、环境和社会科学的人工智能先进与融合(AI-ACCESS)NRT项目。AI-Access计划将准备一批新的调查人员，他们在人工智能、环境科学和社会科学的交叉点接受培训，具有利用人工智能和环境社会科学融合中的协同效应的技能。该计划预计将培训49名博士生，其中包括24名资助的实习生。AI-Access学员将满足组织日益增长的需求，这些组织渴望开发数据驱动的政策和计算算法来应对环境和社会挑战。AI-Access项目利用华盛顿大学在计算机科学、环境科学、环境工程、公共卫生和社会工作方面的研究生课程，开发具有跨学科教育、研究和指导机会的新的变革性培训项目。该项目包括人工智能和机器学习、统计和因果推理以及环境可持续性的预科课程；沟通、团队合作和伦理方面的必修课；以及三个研究方向之一的专业化--计算科学、环境科学和社会科学。该计划还包括招聘工作，重点是通过拓展和挖掘华盛顿大学的REU站点与合作伙伴UHD(一家为少数族裔服务的机构)之间的协同潜力来增加多样性；通过社区建设活动和同行支持计划保持多样性；以及多样性培训努力，以确保所有人工智能接入人员都属于一个多样化、包容性和互联的环境。NSF研究培训(NRT)计划旨在鼓励为STEM研究生教育培训开发和实施大胆的、潜在的变革性新模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Emerging AI and computational tools have the potential to bring about significant transformation of scientific practice, especially in the environmental and social sciences. In fact, the very nature of important questions in those fields are themselves shifting as social systems are increasingly embedded within computational platforms that mediate daily human activity. However, while huge datasets are rapidly becoming commonplace across the environmental and social sciences, the right methods for understanding data generated by human behavior, as well as accessible tools for studying them, are lacking. In response to this urgent need, this National Science Foundation Research Traineeship (NRT) award establishes the AI Advancements and Convergence in Computational, Environmental, and Social Sciences (AI-ACCESS) NRT Program at Washington University in St. Louis (WashU) in collaboration with University of Houston-Downtown (UHD). The AI-ACCESS program will prepare a cohort of new investigators, trained at the intersection of AI, environmental science, and social sciences, with the skills to capitalize on the synergy in the convergence of AI and environmental social science. The program anticipates training forty-nine (49) doctoral students, including twenty-four (24) funded trainees. AI-ACCESS trainees will fill a growing need for organizations that aspire to develop data-driven policies and computational algorithms to address environmental and social challenges.The AI-ACCESS program leverages WashU's graduate programs in computer science, environmental science, environmental engineering, public health, and social work to develop a new transformative training program with transdisciplinary education, research, and mentoring opportunities. The program includes prefatory courses in AI and machine learning, statistical and causal inference, and environmental sustainability; required courses in communication, teamwork, and ethics; and specialization in one of three research tracks -- computational sciences, environmental sciences, and social sciences. The program also includes recruitment efforts focused on increasing diversity through outreach and by exploiting the synergistic potential between the REU site at WashU and partner UHD, a minority-serving institution; diversity retention efforts through community building activities and peer support programs; and diversity training efforts to ensure that all AI-ACCESS personnel belong in a diverse, inclusive, and connected environment.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.