My research has centered on broadening participation in computing (BPC) for almost two decades. This work builds upon a position that the foundation of our current computing education system was designed to attract and retain white male students.
Over the past 20 years, I have created learning experiences intentionally designed for groups underrepresented in computing. What we have learned has implications for computing education and access to learning resources more generally.
Many are concerned with predictive policing and AI-informed prison sentencing. However, AI is also being used every day on more pedestrian decisions, such as helping to determine if we receive insurance, home loans, or jobs.
My current research focuses on three intertwined ways to address these concerns. First, methods for increasing the diversity of voices heard in the design of AI systems. Second, expanding the transparency of these systems. Third, developing learning to increase citizen science knowledge about AI.
The massive increase in data collection accompanying the computer revolution has provided unthought-of benefits. However, these benefits are unequally distributed, and our use of data has created systems of surveillance and discrimination.
Many exceptional scholars are documenting these issues. Building upon their work, my research seeks to understand how we might address these issues through new methods in education and design and the creation of new data tools.