REU Site: Trustworthy AI

Rochester Institute Of Tech, Rochester NY

Investigators

Abstract

Artificial Intelligence (AI) is critical to the United States' economic prosperity and national security. The wide-spread application of AI in many areas dictates that such systems must be made trustworthy. In response to this pressing need, the goal of this award is to encourage ten talented undergraduate students per year to pursue graduate study and research careers in trustworthy AI by engaging them in exciting summer research projects. These research projects are rooted in the faculty mentors' existing, externally funded research in trustworthy AI areas, including robustness, fairness, explainability, privacy, and accountability. All projects feature a different topic in trustworthy AI for a team of two or three undergraduate students to explore under the guidance of the faculty mentors. This immersive research experience is expected to cultivate the participating students' interest in pursuing graduate study in trustworthy AI through their involvement in authentic research efforts. This award makes targeted efforts to recruit students underrepresented in Science, Technology, Engineering, and Mathematics (STEM) careers or who might otherwise be unable to participate in academic research. These students are selected from diverse demographic and economic backgrounds, with specific efforts to include women, underrepresented minorities, and individuals with disabilities (including Deaf/Hard of Hearing students). Faculty mentors train the students to be researchers and provide them with specialized training in the design and development of trustworthy AI systems that are motivated by highly engaging AI innovations for social good. This specialized training provides the students with highly valuable technical and analytical skills that will benefit them in future pursuits in graduate study and industry research and development. The quality of this training is further enhanced by additional professional development activities, including a machine learning crash course, invited speakers, weekly group meetings, and a mentor training workshop for participating faculty mentors. All student participants are given the opportunity to attend and present in academic conferences. The investigators will disseminate their experiences running this program in educational research publications. 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.