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

确定和获得社会服务对于帮助弱势个人和家庭实现自给自足至关重要。社区在很大程度上依赖社会服务组织，不仅满足服务需求，而且成功地将有需要的社区成员转介到适当的服务。这就要求每个服务提供商在任何时候都准确了解服务提供商的整体情况，他们可以提供什么服务，以及向谁提供服务。我们已经确定了这些组织在收集和分享相关信息方面面临的一些基本挑战。 它们包括保持信息的最新，找时间获取具体和隐性知识，并确定组织中知识的关键持有者，仅举几例。 该项目将研究当前人工智能技术进步的潜力，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。 它将推动理解人类与人工智能伙伴关系的界限，并将导致异构网络建模的算法进步，理解人工智能技术背后因素的社会科学进步，以及理解社会工作者在日常工作中与人工智能互动以捕获，维护和利用信息的最有效手段的界面进步。社会服务提供者面临的挑战反映了大型商业组织面临的挑战，这些组织往往将其称为知识管理和转让的挑战。 我们假设KM/KT创新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，并且在当地社区内的分布式社会服务组织中引入KM/KT框架将提供一个独特的机会来建立人工智能（AI）层，以利用这些知识并扩大当地服务提供商的努力。 我们将为本地社会服务提供者开发概念验证统一数据框架，以获取和共享相关服务信息和文件（不包括任何个人客户信息）。 在这个框架上，我们将开发一个AI层，能够对文档进行总结和分类，识别相关知识持有者，提取重要信息，（例如日期、资格要求），以及创建社会服务景观的复杂异构网络模型-所有任务在与服务提供商的专业知识相结合时，可以增强当地社会服务领域提供的服务。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.