ABSTRACT

Rapid developments in artificial intelligence (AI) and machine learning (ML) applications have led to nation-wide calls for supporting youth in the development of artificial intelligence literacy, competencies needed to effectively interact with and critically evaluate artificial intelligence. Most importantly, a broader range of youths must be equipped to understand how these technologies work, their personal and social impacts, and how they may increase or undermine equity. There is an urgent need for research on youth's understanding of AI in everyday contexts that permeate their daily lives, such as interaction with voice assistants or social media applications. In this project high school-aged youth will learn artificial intelligence and machine learning with a focus on the concept of algorithm auditing, a method for understanding an AI algorithm's opaque inner workings by repeatedly querying the AI system in order to interpret its external effects and impacts. This proposal was received in response to the Dear Colleague Letter (DCL): "Rapidly Accelerating Research on Artificial Intelligence in K-12 Education in Formal and Informal Settings (NSF 23-097)" and 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.

Researchers will carry out a one-year study involving high school youth designing everyday machine learning applications for their peers, and collaboratively conducting algorithm audits with a focus on algorithm fairness, accountability, and justice. The following research questions will be examined: (1) What are high school youths' current experiences and understandings of everyday machine learning applications? (2) How do high school youth design and conduct collaborative audits of machine learning applications? And (3) How can high school youth apply algorithm auditing approaches to applications they encounter in their everyday lives? To answer these research questions, groups of diverse high school youth from Philadelphia, including Black, Hispanics and Latinos young people, will work in teams and participate in extended workshops to design and collaboratively audit a variety of machine learning applications with text, sound, and images. Using a combination of co-design, interviews, and observational methods, researchers will gain insights into the feasibility of algorithm audits by youth who are some of these systems' most common users; the dynamics of collaborative interactions for productive end-user algorithm audits; and youth understandings of algorithmic justice through auditing. Previous end-user algorithm auditing research has focused only on non-expert adults. The insights gained about youth's approaches to algorithm audits will generate new knowledge, and also have the potential to inform other artificial intelligence and machine learning literacy efforts.

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.

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