SBIR Phase I: Using ChatGPT and Machine Learning to Power Positive Change among Justice Involved Youth

SBIR 第一阶段：利用 ChatGPT 和机器学习推动参与正义的青少年发生积极变化

• 批准号: 2333168
• 负责人: Sasha Barab
• 金额: $ 27.46万
• 依托单位: LIFELAB STUDIOS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333168&HistoricalAwards=false
• 关键词: SBIR; Phase; Using; ChatGPT; Machine

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to test the benefits of artificial intelligence (AI) to enhance a platform which aids justice-involved youth to develop protective factors for positive change. The project uses advanced machine learning and large language model technologies to optimize the platform's effectiveness and support this vulnerable group, many of whom have adverse childhood experiences, so that they can break the cycle of re-offending. These enhancements empower youth by creating more relevant growth recommendations offering tailored, strengths-based feedback to break the cycle of recidivism and support the transition into productive societal roles. The platform has commercial potential with opportunities for deployment across 3,143 U.S. counties, which collectively serve 800,000 justice-involved youth annually. Successful implementation may alter the life trajectories of these youth, such that they can make positive life changes. This transformation would lessen the economic burden on our already stretched penal systems, while unlocking the potential of these youth to contribute positively to society. This project integrates state-of-the-art machine learning and large language models to fortify an innovative social growth platform, enabling justice-involved youth to make positive life changes. The platform is underpinned by a research-validated growth cycle, and features a dynamic feed governed by intelligent algorithms, specialized protective factor journeys, and a narrative-centric approach that leverages strength-based, social connectivity. The project will facilitate the refinement of the algorithmic architecture behind the dynamic feed to enhance user engagement, thereby promoting protective factors. Large language models will be integrated to serve as an intelligence-augmented mechanism to bolster the strength-based feedback loop within life integration stories.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛/商业影响是测试人工智能（AI）的好处，以增强一个平台，该平台有助于涉及正义的年轻人，以开发积极变革的保护因素。该项目使用先进的机器学习和大型语言模型技术来优化平台的有效性并支持这个脆弱的群体，其中许多人都有不良的童年经历，以便他们可以打破重新犯罪的周期。这些增强功能通过创建更相关的增长建议，提供量身定制的基于优势的反馈，以打破累犯的循环并支持过渡到生产性社会角色。该平台具有商业潜力，并在3,143个美国县进行部署的机会，该县每年共同为80万名司法与正义的青年提供服务。成功的实施可能会改变这些年轻人的生活轨迹，从而可以改变生活的积极生活。这种转变将减轻我们已经伸展的刑法体系的经济负担，同时释放这些年轻人对社会做出积极贡献的潜力。该项目集成了最先进的机器学习和大型语言模型，以加强创新的社会增长平台，从而使与正义的青年能够改变生活。该平台的基础是研究验证的增长周期的基础，并具有由智能算法，专门的保护因素旅程和以叙事为中心的方法控制的动态供稿，该方法利用了基于力量的社会连接。该项目将促进动态供稿背后的算法体系结构的完善，以增强用户参与度，从而促进保护因素。大型语言模型将被整合为一种情报增强的机制，以加强生活整合故事中基于力量的反馈循环。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来通过评估来支持的。