DIVERSITY & INCLUSION
SCHOLARSHIP APPLICATION ESSAY
ACADEMIC MAJOR
Data Science
RESEARCH FOCUS
Algorithmic Fairness
TRUE INCLUSION IS NO LONGER
just about who is invited into the corporate boardroom;
increasingly, it is about who writes the code that governs our daily lives. Growing up in a
historically redlined zip code in Detroit, Michigan, I watched how systemic exclusion evolved
from physical barriers into digital ones. When my parents attempted to refinance our family
home, their application was instantly flagged and denied by an automated underwriting
algorithm. They had perfect payment histories, but the algorithm heavily penalized our
geographic location. In that moment, I realized that bias in the United States had been quietly
automated. Technology was not creating a more level playing field; it was scaling historical
discrimination at an unprecedented speed.
That experience completely redefined my understanding of diversity. I realized that if the
datasets training modern artificial intelligence do not accurately and fairly represent marginalized
communities, the resulting algorithms will weaponize that invisibility against us. I do not just want
a seat at the table; I want to audit the systems that decide who gets through the door.
I am currently a junior pursuing a Bachelor of Science in Data Science with a minor in
Sociology. My career goal is to become an Algorithmic Fairness Researcher in the civic tech
sector. I want to build open-source diagnostic tools that allow municipal governments and public
defenders to audit predictive policing software, automated housing screeners, and loan-approval
algorithms for disparate racial and economic impacts. I want to ensure that the next generation
of artificial intelligence is engineered with equitable, inclusive frameworks from the ground up.
