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

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.