Project Profile

There is an urgent need to develop and implement learning programs that can help teachers to prepare students to effectively interact with and critically evaluate machine learning applications. This project will work with a group of high school teachers across rural, suburban, and urban US communities in California, Delaware and Pennsylvania serving diverse high school students including Black, Latinx, and gender-marginalized young people to design and implement classroom activities that will support students in developing and ?auditing? machine learning applications. The goal of algorithm auditing is to better understand the opaque inner workings of AI systems by repeatedly querying the AI system in order to interpret its external effects and impacts. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.

In the three-year study, 210 high school students will directly participate in the learning of algorithm auditing. Researchers and computer science high school teachers will co-investigate students? machine-learning-powered, sensor-based applications and integrate collaborative algorithm auditing practices to examine fairness, accountability, and justice. The research will be conducted in partnership with public high school teachers from the Exploring Computer Science community and address the following questions: (a) What are high school computer science teachers? values and considerations of algorithmic justice in machine learning applications?, (b) How do high school computer science teachers understand and develop new understandings of algorithmic justice in machine learning applications through auditing?, What implications do teachers find in algorithm auditing for their teaching and with their students?, (c) How do high school computer science teachers integrate and support students? collaborative audits of machine learning applications in classrooms?, and (d) What are high school students? approaches and perspectives when auditing machine learning applications? How does this affect their interests in CS and STEM careers? To answer these research questions, researchers will use a combination of observational methods, interviews and surveys to gain insights into teachers? understandings of algorithm audits, observe high school students designing and auditing machine learning applications, and examine student learning in classroom implementation of auditing activities. Previous research on algorithm auditing for non-experts, including both adults and youth, has only focused on informal settings. The insights gained about teaching and learning algorithm auditing in this school-based investigation can be extended to and inform numerous existing computing activities as well as K?12 computing curricula and teacher professional development.

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.