Theses and Dissertations

Date of Award


Document Type


Degree Name

Master of Science (MS)



First Advisor

Salvatore Restifo

Second Advisor

Steven Foy

Third Advisor

Arlett Lomeli


Algorithm-driven systems are increasingly being used across a variety of settings (Center for Democracy and Technology, 2020; O’Neil, 2017; Sauppé and Mutlu, 2015). However, many everyday technologies are imbued with racial biases that unfairly discriminate against racially marginalized groups (Hankerson et al, 2016; Crawford, 2016; O’Neil, 2017). While sources of bias like the unintended overrepresentation of elements in an algorithm’s dataset have been well researched (Hankerson et. al, 2016; Lee, 2015), the transfer of personal prejudices onto a system remains understudied. Here I address this issue and draw from Sociological literature on organizational theory, assimilation, and colorblind ideology to develop a quasi-experimental study that investigates the relationship between racial biases held by programmers and the algorithms they create. Via a mixed-methods survey conducted on Computer Science and Computer Engineering undergraduate students at a Hispanic-serving institution, I examine the effects of demographics, cultural background, and color blind beliefs on a series of hypothetical programming tasks. The study reveals valuable and novel insights on programmer bias transfer and allows for further development of the topic.


Copyright 2023 Ana Cecilia Sánchez Ramos. All Rights Reserved.

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