SBIR Phase I: An inclusive machine learning-based digital platform to credential soft skills

SBIR 第一阶段：一个基于机器学习的包容性数字平台，用于认证软技能

• 批准号: 2317077
• 负责人: Geeta Verma
• 金额: $ 27.5万
• 依托单位: LIVEDX INC.
• 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: 2024-01-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317077&HistoricalAwards=false
• 关键词: SBIR; Phase; inclusive; machine; learning

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to enable people who aspire to higher education and/or career opportunities to create a demonstrable portfolio of soft skills based on their lived experiences. Soft skills (e.g., problem-solving, teamwork, leadership, etc.) are as important as hard skills for individual success. However, current soft-skill assessment tools are subjective, inefficient, and inconsistent. This is especially painful for marginalized populations such as minorities and women, who often possess valuable soft skills such as stress management and conflict resolution, but do not have the tools to demonstrate it. The proposed solution will change how people’s lived experiences and the soft skills associated to those experiences are valorized. This technology may open the door to better educational and professional opportunities in the U.S., to increased economic competitiveness (since higher education plays an increasingly critical role in the economic competitiveness of a nation), to advanced health and welfare of the American public (since adults with higher education often live healthier and longer lives, and enjoy better financial situations), and to a more developed and diverse STEM workforce (by focusing on valorizing the social and cultural capital of minoritized students).This project proposes a digital platform that provides soft-skill credentialing guided by lived experiences. The main innovation behind the proposed solution is a proprietary system that combines Machine Learning (ML) and Natural Language Processing to analyze the candidate’s experiences and apply different evidence-based social-emotional assessment frameworks to accredit the soft skills embedded in each experience. This solution may be the first time a proprietary ML technology will be integrated with a large language model to provide soft-skill credentialing upon lived experiences. The main technical challenge is avoiding bias in the assignation of soft-skill credentials. Other technical challenges are: 1) the potential scarcity of training data; 2) the correct definition of credential categories; and 3) the ability to explain the ML models. This project is intended to address these challenges by 1) developing a proof-of-concept prototype of the accreditation model; 2) conducting a preliminary analysis of its fairness when assessing marginalized groups; 3) reformulating the accreditation algorithm in case any bias is detected; and 4) evaluating, with real datasets, the performance of the credential classifier, the bias mitigation strategies, and the explanations generated for each assessment.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.

这个小企业创新研究 (SBIR) 第一阶段项目的更广泛/商业影响是使那些渴望接受高等教育和/或职业机会的人能够根据他们的生活经验（例如，软技能）创建可证明的软技能组合。解决问题的能力、团队合作、领导力等）与个人成功的硬技能一样重要，然而，当前的软技能评估工具是主观的、低效的和不一致的，这对于少数族裔和女性等边缘群体来说尤其痛苦。经常拥有贵重物品的人压力管理和冲突解决等软技能，但没有工具来证明它。所提出的解决方案将改变人们的生活经历以及与这些经历相关的软技能的评估方式。这项技术可能会为更好的教育和发展打开大门。提高美国的职业机会、提高经济竞争力（因为高等教育在一个国家的经济竞争力中发挥着越来越重要的作用）、提高美国公众的健康和福利（因为受过高等教育的成年人通常生活得更健康、寿命更长）并享受更好的财务状况），以及更发达和更多样化的 STEM 劳动力（通过专注于评估少数族裔学生的社会和文化资本）。该项目提出了一个数字平台，提供以生活经验为指导的软技能认证。所提出的解决方案背后的主要创新是专有的。该系统结合了机器学习 (ML) 和自然语言处理来分析候选人的经历，并应用不同的基于证据的社交情感评估框架来认证每个经历中嵌入的软技能。该解决方案可能是第一次专有的 ML 解决方案。技术将与大型语言模型集成，以根据生活经验提供软技能认证。 主要的技术挑战是避免软技能认证分配中的偏见：1）培训数据的潜在稀缺；2） ) 证书类别的正确定义；以及 3) 解释机器学习模型的能力，该项目旨在通过 1) 开发认证模型的概念验证原型；2) 进行初步分析。评估边缘化群体时的公平性；3) 重新制定认证算法，以防发现任何偏差；4) 使用真实数据集评估证书分类器的性能、偏差缓解策略以及每次评估生成的解释。授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。