CAP: Semi-supervised Fairness-Enhanced Knowledge Graph Construction on Social Media for AI-Enhanced Juvenile Justice

该项目是ExpandAI能力建设试点（CAP），其重点是通过利用AI技术和社交媒体数据，在Prairie View AM University建立和发展AI相关活动，以显著增强暴力预防和为青少年提供司法服务。社交媒体是煽动肢体冲突的帮派青年以及分享暴力和帮派冲突信息的社区成员的主要信息来源。通过Prairie View A M大学工程学院和少年司法学院之间的合作，将使用从社交媒体提取的数据构建公平增强的知识图。这些知识图（KG）是智能系统用于解决复杂问题的事实表示的构造。 幼儿园将消除偏见，并用于揭示青年暴力循环的机制、后果和当地知识。该项目预计将显着提高人工智能的研究和教学能力的草原视图A M大学-HBCU。该项目将促进公平和加强暴力预防，促进自然语言处理，计算机视觉，机器学习和可信人工智能领域的使用启发式人工智能研究。在教育方面，计划的活动不仅使非洲裔美国学生获得重要的尖端跨学科技能，而且还大大扩大了COE和COJJ学生的职业道路。公平性增强的知识图谱构建过程涉及几项任务，包括（i）增强社交媒体平台上呈现的司法相关数据的公平性;（ii）半监督FKG构建;以及（iii）全面的FKG质量评估和工具开发。一种基于人在回路的群体学习方法将用于将用户反馈整合到学习中，以促进假新闻检测。在知识图构建过程中，将采用去偏置技术来提高公平性。该项目将修改两所学院现有课程的内容，为学生提供指导，并为与司法应用相关的人工智能竞赛提供培训。通过与NVIDIA Deep Learning Institute等外部组织合作，将开发用于人工智能培训的教学工具包，此外，还将向社区，HBCU及其他地区进行宣传。该项目由历史上的黑人学院和大学本科生计划（HBCU-UP）共同资助，该计划为加强HBCUs的STEM本科教育和研究提供奖励。ExpandAI计划支持少数族裔服务机构的人工智能驱动的教育和劳动力发展、基础设施和研究，以加强和多样化美国的研究和教育途径，并为历史上边缘化的社区提供STEM职业的新机会。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力审查标准进行评估，被认为值得支持。

This project is an ExpandAI Capacity building pilot (CAP), which focuses on establishing and growing AI related activities at Prairie View A & M University by utilizing AI technology and social media data for significant enhancement of violence prevention and delivery of justice to juveniles. Social media is a major source of information for gang-associated youth who instigate physical conflicts as well as community members who share information about violence and gang conflicts. Through a collaboration between Prairie View A&M University College of Engineering and College of Juvenile Justice, fairness-enhanced knowledge graphs will be constructed using data extracted from social media. These knowledge graphs (KG) are constructs for the representation of facts used by intelligent systems for the solution of complex problems. KGs will be de-biased and used to uncover mechanisms for, consequences of, and local knowledge about the cycle of youth violence. The project is expected to significantly enhance the AI research and instruction capacity of Prairie View A&M University – an HBCU. The project will promote fairness and enhance violence prevention, catalyzing research in use-inspired AI in the fields of natural language processing, computer vision, machine learning and trustworthy AI. On the educational side, the activities planned will not only enable African American students to acquire important cutting-edge cross-disciplinary skills but also significantly expand the career pathways for both COE and COJJ students. The fairness-enhanced knowledge graph construction process involves several tasks including (i) enhancement of the fairness of justice-related data represented on social media platforms; (ii) semi-supervised FKG construction; and (iii) comprehensive FKG quality assessment and tool development. A human-in-the-loop based swarm learning approach will be used to integrate user feedback into learning to facilitate fake news detection. De-biasing techniques will be employed to enhance fairness during Knowledge Graph construction. The project will revamp elements of existing curricula across both colleges, provide mentoring for students and training for AI competitions related to justice applications. In collaboration with external organizations like NVIDIA Deep Learning Institute, teaching kits for AI training will be developed, furthermore, there will be outreach to the community, HBCU and beyond. This project is co-funded by the Historically Black Colleges and Universities Undergraduate Program (HBCU-UP), which provides awards to strengthen STEM undergraduate education and research at HBCUs. The ExpandAI Program supports AI-powered education and workforce development, infrastructure and research at Minority Serving Institutions to strengthen and diversify U.S. research and education pathways and provide historically marginalized communities with new opportunities in STEM careers.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.