Understanding Post-Baccalaureate Cultural Gaps: Building Equitable Ecosystems for AI Research and What We Can Learn from Federal TRIO Programs

This paper aims to survey the problem space around cultural barriers in research collaboration, specifically for Machine Learning (ML). We review (1) unequal representation in ML/AI and STEM, (2) socioeconomic influences on retention of scientists and researchers, and (3) existing educational opportunity programs for people from underresourced backgrounds, with emphasis on Post-Baccalaureate support. We provide evidence that scientists from disadvantaged backgrounds not only experience barriers to gaining intellectual and technical expertise, but also often experience cultural gaps that impede their inclusion in research collaborations. We discuss relevant research on culture differences and the ways that some U.S. Federal TRIO programs explicitly address them, highlighting standardization as one means of demystifying academic and research cultures. We conclude with recommendations toward understanding post-education culture gaps, with the goal of finding better solutions for increasing diversity in research collaborations.

• Machine Intelligence

• Responsible AI