Date of Award
University Scholars Director
First Advisor/Committee Member
Second Advisor/Committee Member
Data Science, Data Ethics, Algorithms, Practices, Institutional Racism, Manipulation
This paper encompasses an examination of defective data collection, algorithms, and practices that continue to be cycled through society under the illusion that all information is processed uniformly, and technological innovation consistently parallels societal betterment. However, vulnerable communities, typically the impoverished and racially discriminated, get ensnared in these harmful cycles due to their disadvantages. Their hindrances are reflected in their information due to the interconnectedness of data, such as race being highly correlated to wealth, education, and location. However, their information continues to be analyzed with the same measures as populations who are not significantly affected by racial bias. Not only can the data itself be manipulated by collection methods, but faulty algorithm design and poor practices and implementation can intensify the damage. Such as lack of: regulation, racial literacy, peer review, expiration of data, privacy retention, and more. There is a denial of privacy for those who are vulnerable such that they cannot afford privacy and require more attention in general, which leads to over-surveillance. Additionally, there is an imbalance of value between the analytic information of subjects and the actual humans creating such data, so that the value of bodies is diminishing in quantitative arenas.
cockerell, Gabrialla S., "Data Ethics: An Investigation of Data, Algorithms, and Practice" (2022). Honors Projects. 157.
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