Boyang “Rowena” Tang, GSE Masters in Learning Science & Technologies ‘24, China
During the summer, I worked as a research assistant at the University of Pennsylvania, Graduate School of Education on the Project named Auditing Machine Learning Applications for Algorithmic Justice with Computer Science High School Students and Teachers.
This three-year research project, stressing on an urgent need to educate both teachers and students about the ethical and justice-related implications of AI/ML technologies in this industry 4.0 era, aims to integrate the concept of algorithm auditing into high school computer science education. Algorithm auditing involves critically examining the opaque inner workings of AI systems to understand their external effects and impacts. The project will work with high school teachers and students from diverse backgrounds in California, Delaware, and Pennsylvania, focusing on communities that include Black, Latinx, and gender-marginalized individuals. The goal is to design and implement classroom activities that support students in developing and auditing machine learning (ML) applications. As a research assistant, I am honored to participate in its initial stage and was mainly responsible for data collection and management.
Before the actual workshop, we conducted pre-interviews with teachers, mainly proposed for understanding high school computer science (CS) teachers’ values and considerations of algorithmic justice in machine learning applications and how they develop new understandings of algorithmic justice through auditing. When doing the clearance of the interview, I was prepared to gain a better understanding of each teacher and offer supports during the actual week-long workshop process. During the workshop, I not only grasped professional knowledge the algorithmic justice itself but also learned the ways to combine algorithmic justice thinking into the current K-12 educational system. Apart from the academic gains, I developed practical experience in social science qualitative research including using observational methods, interviews, and surveys to gather insights into teachers’ and students’ understandings and practices. Since the project involves co-designing sensor-based ML applications and auditing activities with high school teachers and students and these activities will be piloted and revised over a three-year period to integrate algorithmic justice issues into teaching and learning, I also learned about the organization and planning of the long-term research project.
This internship was a great opportunity to obtain experience in research about learning science and education fields and I am eager to build upon the data we collected and the insights we gained. I plan to leverage this valuable data to further explore the nuances of algorithmic biases and how complicated concepts can be better taught. Thus, a general framework will be developed to be applied to different scenarios. I aim to conduct a more detailed analysis of the datasets we gathered during the workshop. Furthermore,It is also worthy to compare the pre-interview data and the interactive data between teacher and students. By developing workshops and curricula that incorporate our findings, we can help teachers integrate algorithmic justice into their computer science classes more effectively. I am looking forward to following up and participating in this project in the future.
This is part of a series of posts by recipients of the 2024 GAPSA Summer Internship Funding Program that is coordinated by Penn Career Services. We’ve asked funding recipients to reflect on their summer experiences and talk about the industries in which they spent their summer. You can read the entire series here.
