Teaching an Intersectional Data Analysis on Affirmative Action.

ADM systems can be used to perform a task as inconsequential as recommending a song on Spotify, to making a decision that is instrumental to someone's life, such as determining their candidacy for college. If an algorithm is trained on biased data, it can propagate prejudice. Thus, it is pertinent to find methods to decrease ADM bias. This paper presents a way to potentially mitigate ADM bias by teaching high school students a intersectional data analysis activity that incorporates the second pillar of the liberatory computing framework, critical consciousness. This activity is designed to enable high school students to understand the bias and history behind the college admission process, which allows students to develop a critical consciousness. Establishing a critical consciousness will diversify the computing field and the data incorporated into ADM systems by encouraging minoritized high school students to get a degree in computer science. The National Institute of Standards and Technology (NIST) suggests that diversifying the computing field has the potential to reduce bias in ADM systems. Thus, the activity is focused on students developing a critical consciousness. This paper discusses the preliminary findings from teaching a two-day computing activity to high school students.