Collaborative Research: FW-HTF-P: Assistive Artificial Intelligence for Diversifying and Reskilling the Disaster Management Workforce of the Future

由于气候变化，灾害影响变得更加频繁和严重，对弱势群体造成了不成比例的伤害，包括妇女、有色人种社区、残疾人、收入困难社区，以及更普遍的那些无法为自己辩护的人。来自这些人群的人在灾害管理专业中的代表性也很低。这一事实增加了灾害管理做法与更广泛社区的需求不一致的风险，招聘和资源分配方面的偏见将加剧这些脆弱性。该项目的目标是为这些弱势群体提供更多灾害管理工作机会。通过将居民、社区领袖以及州和地方当局联系起来，该项目将产生关于以人为中心的人工智能(AI)如何通过促进劳动力的多样化和再技能、最终增强人类能力并导致对文化敏感的团队和公平的灾害管理实践来改变灾害管理专业的新知识。该项目的技术目标围绕一系列相互关联的规划活动，这些活动促进了趋同的研究路线图，发展了基础研究概念，并激发了解决灾害管理领域的技术、人类、社会和经济维度的研究能力。该项目团队将研究、正式确定并展示人工智能在促进灾害管理人员队伍和实践中的多样性和包容性方面的潜力。通过将组成顾问委员会的利益相关者的积极参与，项目团队将动员来自灾害易发社区的居民和领导人、州和地方灾害管理机构以及领域专家和研究人员制定全面的路线图，指导灾害管理专业辅助人工智能技术的设计、测试和传播。规划活动将包括一次关于灾害管理中的多样性和包容性的讲习班、一次利益攸关方会议和演示，以及研究工作组和协作会议。总之，这些活动将有助于实现三个研究目标：(1)促进多学科研究和利益攸关方伙伴关系，采用灾害管理、学习科学、计算机科学、工程学、劳动力培训和社会科学的联合视角、方法和知识；(2)加深对灾害管理中人类-人工智能伙伴关系的理解，以增加(而不是取代)人类工人，包括考虑劳动力多样性以及如何传授与人工智能互动所需的技能；以及(3)了解、预测和探索减轻人工智能融入灾害管理劳动力培训和再技能所产生的潜在技术和社会风险的方法。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。

Disaster impacts, which have become more frequent and severe due to climate change, disproportionately hurt vulnerable populations, including women, communities of color, people with disabilities, income-challenged communities, and more generally those who are not able to advocate for themselves. People from these populations are also poorly represented in the disaster-management profession. This fact raises risks that disaster-management practices do not align with the needs of the broader community, and that biases in hiring and resource allocation will reinforce these vulnerabilities. The goal of this project is to increase disaster-management job opportunities for people from these vulnerable groups. Through connecting residents, community leaders, and state and local authorities, this project will result in new knowledge about how human-centered artificial intelligence (AI) can transform the disaster-management profession by contributing to the diversification and reskilling of the workforce, ultimately augmenting human capabilities, and leading to culturally sensitive teams and equitable disaster-management practices.The technical aims of the project center around a series of interconnected planning activities that foster a convergent research roadmap, develop fundamental research concepts, and stimulate research capacity that address technological, human, societal, and economic dimensions of the field of disaster management. The project team will study, formalize, and demonstrate the potential of AI to promote diversity and inclusion in disaster-management workforce and practices. Through active participation of stakeholders who will form an advisory board, the project team will engage residents and leaders from disaster-prone communities, state and local disaster-management agencies, and domain experts and researchers to develop a comprehensive roadmap that will guide the design, testing, and dissemination of assistive AI technologies for the disaster-management profession. Planning activities will include a workshop on diversity and inclusion in disaster management, a stakeholder meeting and demonstrations, and research working groups and collaboration meetings. Together, these activities will help meet three research objectives: (1) Facilitate multidisciplinary research and stakeholder partnerships that employ the joint perspectives, methods, and knowledge of disaster management, learning sciences, computer science, engineering, workforce training, and the social sciences; (2) Impart deeper understanding of human-AI partnership in disaster management that can augment (not replace) human workers, including consideration of workforce diversity and how to impart skills needed to interact with AI; and (3) Understand, anticipate, and explore ways of mitigating potential technological and societal risks resulting from AI integration into disaster-management workforce training and reskilling.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.