SCC-PG: Human-AI Partnership for Knowledge Management and Transfer in Community Social Services

SCC-PG：社区社会服务知识管理和转移的人机合作

• 批准号: 2331007
• 负责人: Ronald Metoyer
• 金额: $ 14.98万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331007&HistoricalAwards=false
• 关键词: SCC; PG; Human; AI; Partnership

Identifying and accessing social services is critical in aiding vulnerable individuals and families in their move toward self-sufficiency. Communities rely heavily on social service organizations to not only fulfill service needs but to successfully refer community members in need to appropriate services. This requires that each service provider have an accurate understanding of the larger landscape of service providers, what services they can provide, and to whom, at any point in time. We have identified a number of fundamental challenges that these organizations face with regard to the collection and sharing of relevant information. They include keeping information up-to-date, finding time to capture concrete and tacit knowledge, and identifying key holders of knowledge in the organization, just to name a few. This project will examine the potential for the current advances in AI technology to be applied to help social service organizations become more efficient in support of the most vulnerable members of our communities. It will push the boundaries of understanding human-AI partnerships and will result in algorithmic advances in heterogeneous network modeling, social science advances in understanding the factors behind the uptake of AI technology, and interface advances in understanding the most effective means for social workers to engage with AI to capture, maintain, and leverage information in their everyday work. The challenges of social service providers mirror those seen in large commercial organizations which often refer to them as challenges of knowledge management and transfer (KM/KT). We hypothesize that KM/KT innovations can be successfully applied to the social services sector, drastically improving service in our communities and that the introduction of a KM/KT framework among the distributed social service organizations within a local community will provide a unique opportunity to establish an artificial intelligence (AI) layer to leverage that knowledge and amplify the efforts of local service providers. We will develop a proof-of-concept unified data framework for local social service providers to capture and share relevant service information and documents (excluding any personal client information). Over this framework, we will develop an AI layer capable of summarizing and classifying documents, identifying relevant knowledge holders, extracting important information (e.g. dates, eligibility requirements), and creating a complex heterogeneous network model of the social services landscape – all tasks that when combined with service provider expertise, can enhance the services offered across the local social services landscape.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.

确定和访问社会服务对于帮助弱势个人和家庭迈向自给自足至关重要。社区在很大程度上依靠社会服务组织不仅满足服务需求，而且还可以成功地推荐有需要的社区成员提供适当的服务。这要求每个服务提供商都能准确了解服务提供商的较大景观，他们可以提供的服务以及在任何时间点向谁提供服务。我们已经确定了这些组织在收集和共享相关信息方面面临的许多基本挑战。它们包括保持最新信息，寻找时间来捕获具体和默认知识，并确定组织中知识的关键持有者，仅举几例。该项目将研究AI技术目前进步的潜力，以帮助社会服务组织更有效地支持我们社区中最脆弱的成员。它将推动理解人类合作伙伴关系的界限，并将在异质网络建模，社会科学的进步方面取得算法的进步，理解采用AI技术的因素的进步，以及了解社会工作者与AI互动以捕获，维持和维持每天工作的信息的最有效手段的界面进步。社会服务提供商的挑战反映了在大型商业组织中看到的挑战，这些组织通常将其称为知识管理和转移的挑战（KM/KT）。我们假设可以成功地应用于社会服务部门，大大改善我们社区的服务，并且在当地社区内分布式社会服务组织中引入KM/KT框架将提供独特的机会，以建立人工智能（AI）层，以利用知识和娱乐的本地服务代表。我们将在这个框架上，我们将开发一个能够汇总和分类的AI层，确定相关知识持有人，提取重要信息（例如日期，资格要求），并创建一个复杂的社会服务环境网络模型 - 当与服务提供者的专业知识相结合时，都可以促进服务范围的服务。使用基金会的智力优点和更广泛的影响标准，认为通过评估被认为是宝贵的支持。