REU Site: Computational Sensing for Human-centered AI

以人为中心的人工智能计算感知(REU)网站的本科生研究体验网站认识到，人机交互(HCI)和人工智能(AI)之间的界限正在变得模糊，人工智能正变得越来越灵活和普遍。因此，下一代计算科学家必须能够负责任和有效地利用来自数据生成人类的一系列传感数据。通过这一重点，REU网站将扩大其作为高度多样化学生的吸引力的轨迹，包括女性或性别少数群体和残疾学生，他们将获得传感硬件和软件方面的经验，以实现专注于人类行为和认知过程的智能系统的变革性进步。让不同的利益相关者在职业生涯早期就能发现如何收集、融合、推断多模式人类数据并将其可视化，这有助于培养一支能够改变人类和机器参与和协作方式的员工队伍。REU网站上的这项研究将解决人工智能的两个局限性：第一，目前专注于大数据人工智能，服务不足的人群面临被边缘化的风险；第二，人工智能学员往往缺乏收集人类数据的经验，以及对人类启发的数据集的批判性思考。REU网站将激发新的、安全的方式，让系统与人们合作，解决社会的棘手问题，同时保持公平，适应偏见，并代表普通人口的多样化结构。以人为中心的人工智能计算传感REU网站设定了三个雄心勃勃且可实现的目标，并将在计算和科学、技术、工程和数学(STEM)研究和教育场所广泛发表研究成果：(1)推进以人为中心的人工智能计算传感的基础研究，将人类研究与专注于适度规模、包容的数据集的合理人工智能实验相结合；(2)开发编程机制，帮助学生自信地从研究人员的教学过渡到由研究人员指导，并确保他们准备进入计算机科学/STEM博士项目；以及(3)改进和强化我们的招生策略，目的是将我们的申请者中的原住民或拉丁裔学生和残疾学生增加一倍，并为异常多样化的群体实现更严格的选择目标。我们将这些目标与四个相关的网站目标联系起来：(A)提供基于团队的研究，强调社会公益和影响的真实世界问题；(B)通过促进REU队列的校园内国际文化交流，以成本效益的方式促进全球意识和技能建设；(C)为队列提供外展丰富，学习如何广泛地交流研究；以及(D)向科学界传播从REU网站获得的经验教训。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。

The Research Experiences for Undergraduates (REU) Site in Computational Sensing for Human-centered Artificial Intelligence recognizes that the boundaries between human-computer interaction (HCI) and artificial intelligence (AI) are blurring and that AI is growing increasingly agile and pervasive. Thus, the next generation of computational scientists must be capable of responsibly and effectively leveraging a spectrum of sensing data from data-generating humans. With this focus, the REU Site will expand its trajectory as an attractor for highly diverse students, including women or gender minorities and students with disabilities, who will gain experience with sensing hardware and software towards transformative advances in intelligent systems focused on human behaviors and cognitive processes. Enabling diverse stakeholders early in their careers to discover how to collect, fuse, make inference with, and visualize multimodal human data helps nurture a workforce that can transform how humans and machines engage and collaborate. The research in the REU Site will address two limitations in AI: first, that underserved populations are at risk of being marginalized with the present focus on big data AI and, second, that AI trainees often lack experience with human data collection and critical thinking about human-elicited datasets. The REU Site will stimulate novel, safe ways for systems to team up with people to address society's vexing problems while remaining fair, attuned to bias, and representative of the diverse fabric of the general population. The REU Site in Computational Sensing for Human-centered Artificial Intelligence sets three ambitious and attainable goals and will publish widely on the findings at computational and Science, Technology, Engineering, and Mathematics (STEM) research and education venues: (1) to advance basic research in computational sensing for human-centered AI, integrating human study with sound AI experimentation focused on modestly-sized, inclusive datasets; (2) to develop programmatic mechanisms for aiding students to transition confidently from being taught to being mentored by research faculty and ensure their readiness for entering computer science/STEM PhD programs; and (3) to enhance and intensify our recruiting strategies with the aim of doubling Native American or Latina/o students and students with disabilities among our applicant pool, as well as reach even more demanding selection targets for exceptionally diverse cohorts. We link these objectives to four associated Site aims: (a) to offer team-based research with real-world problems that emphasize social good and impact; (b) to cost-effectively enable global awareness and skills-building, by facilitating on-campus international cultural exchange for REU cohorts; (c) to provide outreach enrichment for cohorts to learn how to communicate research broadly; and (d) to disseminate lessons learned from the REU Site to the scientific community.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.