How Algorithms are Reinforcing Oppressive views towards Black Women – UROP Spring Symposium 2021

How Algorithms are Reinforcing Oppressive views towards Black Women

Alia Cummings

Alia Cummings

Pronouns: She/her/hers

Research Mentor(s): Sarita Yardi Schoenebeck, Associate Professor
Research Mentor School/College/Department: School of Information,
Presentation Date: Thursday, April 22, 2021
Session: Session 6 (4pm-4:50pm)
Breakout Room: Room 16
Presenter: 5

Event Link


The internet and search engines are used daily by most people in the U.S. However, most users are not familiar with the idea of algorithmic bias and how algorithms impact our communities and society. Algorithmic biases and the ethics of various technological algorithms can affect how gender, race, class systems, and many other categories are viewed and experienced from a societal lens. This project investigates the concepts of bias, discrimination, and ethics in current and emerging technological algorithms. This research will explore how Black Women are viewed from a societal and historical standpoint when searched for in various internet search engines and how these search results affect Black Women from an oppressive standpoint. From this investigation, educational YouTube content is created along with lesson plans based on the topic to be taught in the classroom. These lesson plans are taught in two separate classrooms, one in Detroit, MI with a majority African American community and the other in Ann Arbor, with a majority white population. An analysis of these two interactions along with reports of what has been learned about algorithmic bias and visuals gathered, awareness of the topic will be spread along with looking to find solutions to the algorithmic problems at hand. This research is able to provide a critical understanding of algorithms in technology. From this research one can see how bias, discrimination, and ethics show up in the media we constantly use and how these biases affect various societal communities. Minorities and social groups will also be able to heavily benefit from this research along with the companies that are creating these algorithms.

Authors: Alia Cummings, Sarita Schoenebeck
Research Method: Library/Archival/Internet Research

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