NRT-AI: Digital Transformation of Development (DToD)

在过去的二十年里，从收入和气候到住房和健康，人类生活的各个方面都有大量的数字信息。结合物联网的这些数据，并利用人工智能/机器学习（AI/ML）分析它们的技术，现在被认为对所有领域的进步至关重要。数字化转型是工业和商业多个部门的主要内容。然而，数字化转型对减贫、人道主义援助、救灾以及更广泛地改善资源不足或低收入人群的成果的更深远影响却没有得到充分研究。这个国家科学基金会研究培训（NRT）奖给加州大学伯克利分校将使我们能够为我们的学生在设计方法，算法和解决问题的方法提供跨学科的培训，以实质性地分析，道德调查，并从数据集和模型中开发可验证的，公平的，包容的和有用的观察。从这些见解中，学生将能够设计和部署数字化工具，以加速美国和全球低收入人口的增长。该项目预计将培训100名硕士和博士生，其中包括28名NRT资助的学员，他们来自该大学的任何学位课程，对研究发展的数字化转型（DToD）感兴趣。一个关键的问题是，使用大数据开发的分析和其他机制如何使被调查的人群受益。通过确保分析技术和算法，隐私，公平，机制设计和数字技术的实施与服务不足的人群相关，DToD培训还有望增加STEM中代表性不足的少数群体成员的多样性和包容性。DToD计划将包括跨学科研究活动，正式课程和课程建设，沉浸式实验室和实地培训，在博士和硕士学位水平上创建官方DToD浓度，以及职业发展。此外，该计划在为跨国劳动力开发关键技能方面的有效性将通过形成性评估进行评估。该计划将依靠跨学科的研究人员和发展实践者在三个广泛的应用领域：（1）减轻贫困的措施与数据，算法和AI应用于政策;（2）贫困干预，整合自然和建筑环境在低资源设置;和（3）改善健康和福祉在低收入人群。DToD还将有助于开发与推进AI相关的国家战略和优先事项相一致的共享公共数据集。NSF研究培训计划（NRT）旨在鼓励开发和实施大胆的，新的，具有潜在变革性的STEM研究生教育培训模型。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相一致的综合培训模式，在高优先级的跨学科或融合研究领域有效培训STEM研究生。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Over the past two decades, a massive amount of digital information has become available on every aspect of human existence, from income and climate to housing and health. Combining these data from the Internet of Things, and drawing on techniques for analyzing them through Artificial Intelligence/Machine Learning (AI/ML), is now considered crucial for progress in all sectors. Digital transformation is the staple of multiple sectors of industry and commerce. However, more far-reaching consequences of digital transformation for poverty alleviation, humanitarian assistance, disaster relief, and, more generally, improving outcomes for under-resourced or low-income populations are understudied. This National Science Foundation Research Traineeship (NRT) award to the University of California-Berkeley will allow us to provide interdisciplinary training for our students in design methodologies, algorithms, and problem-solving approaches to substantively analyze, ethically investigate, and develop verifiable, fair, inclusive, and useful observations from datasets and models. From these insights, students will be able to devise and deploy digital tools to accelerate growth for low-income populations in the U.S. and globally. The project anticipates training 100 master’s and doctoral students, including 28 NRT-funded trainees, from any degree program at the university that expresses interest in researching digital transformation of development (DToD). A key concern has been how the analytics and other mechanisms developed using big data benefit the populations surveyed. By ensuring that the analytic techniques and algorithms, privacy, fairness, mechanism design, and implementation of digital technology are relevant to under-served populations, the DToD traineeship also expects to increase diversity and inclusion of members of underrepresented minority groups in STEM.The DToD program will include interdisciplinary research activities, formal coursework and curriculum building, immersive lab and field training, the creation of official DToD concentrations at both the doctoral and master’s degree levels, and career development. In addition, the program’s effectiveness in developing critical skillsets for multinational workforces will be assessed through formative evaluation. The program will rely on both interdisciplinary research faculty and development practitioners in three broad application areas: (1) Measures for Alleviating Poverty with Data, Algorithms, and AI Applied to Policy; (2) Poverty Interventions that Integrate the Natural and Built Environments in Low-Resource Settings; and (3) Improving Health and Wellbeing in Low-Income Populations. DToD will also contribute to the development of shared public datasets aligned with national strategies and priorities related to advancing AI.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.