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.

这个小型企业创新研究(SBIR)第一阶段项目的更广泛/商业影响是测试人工智能(AI)的好处，以增强一个帮助参与司法的青年发展积极变化的保护因素的平台。该项目使用先进的机器学习和大语言模型技术来优化平台的有效性，并支持这一弱势群体，他们中的许多人都有不良的童年经历，这样他们就可以打破再次犯罪的循环。这些改进通过创建更相关的成长建议来增强青年的能力，提供量身定制的、基于优势的反馈，以打破惯犯的循环，并支持向生产性社会角色的过渡。该平台具有商业潜力，有机会在美国3143个县部署，这些县每年总共为80万名参与司法的青年提供服务。成功的实施可能会改变这些年轻人的生活轨迹，使他们能够做出积极的生活改变。这一转变将减轻我们本已捉襟见肘的刑罚制度的经济负担，同时释放这些青年为社会作出积极贡献的潜力。该项目整合了最先进的机器学习和大型语言模型，加强了创新的社会成长平台，使涉正义的青年能够做出积极的生活改变。该平台以研究验证的增长周期为基础，具有由智能算法管理的动态订阅源、专门的保护因素旅程，以及利用基于实力的社交连接的以叙事为中心的方法。该项目将促进改进动态订阅源背后的算法架构，以提高用户参与度，从而促进保护因素。大型语言模型将被整合起来，作为一种智能增强机制，以支持生活整合故事中基于强度的反馈循环。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。