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

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高等教育和/或职业机会的人能够根据他们的生活经验创建一个可证明的软技能组合。软技能（如解决问题、团队合作、领导能力等）与硬技能对个人成功同样重要。然而，当前的软技能评估工具是主观的、低效的和不一致的。这对少数民族和妇女等边缘化人群来说尤其痛苦，她们往往拥有管理压力和解决冲突等宝贵的软技能，但却没有展示这些技能的工具。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价值。这项技术可能为在美国获得更好的教育和职业机会、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起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往往活得更健康、更长寿，并且享有更好的经济状况）、以及更发达、更多样化的STEM劳动力（通过关注少数族裔学生的社会和文化资本的增值）。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。提出的解决方案背后的主要创新是一个专有系统，它结合了机器学习（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会情感评估框架来认证每种经历中嵌入的软技能。该解决方案可能是首次将专有的机器学习技术与大型语言模型集成在一起，以提供基于实际经验的软技能认证。主要的技术挑战是在分配软技能证书时避免偏见。其他技术挑战包括：1)训练数据的潜在稀缺性；2)正确定义证书类别；3)解释ML模型的能力。该项目旨在通过以下方式解决这些挑战：1)开发认证模型的概念验证原型；2)在评估边缘群体时，对其公平性进行初步分析；3)重新制定认证算法，以防发现偏差；4)使用真实数据集评估凭证分类器的性能、偏差缓解策略以及每个评估产生的解释。该奖项反映了美国国家科学基金会的法定使命，并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。

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.