An Exploration of Misinformation in Data and its Effect on the Civil War in Ethiopia

Addis, Rodas, School of Engineering and Applied Science, University of Virginia
Ferguson, Sean

The following technical and STS theses relate to representation of data and the power it holds on a narrative. Specifically, the technical thesis dealt with generating effective data science models for IBM’s federal clients in the Data and AI scope. My technical topic is loosely related to the STS thesis as they are both related to data as a form of representation and the messages they can portray about the experience and needs of a demographic thereof.

Being brought up in the 21st century, disparities in how Black bodies are portrayed in the media has been apparent to me from a young age. Data manipulation is ubiquitous; it becomes particularly marginalizing to nations and groups of people who are socio- economically and technologically disadvantaged. I have experienced the power of the digital arena when it comes to portraying narratives of humanitarian and racial crises across the globe. From my own accounts of the civil war in Ethiopia juxtaposed with the Western media’s portrayal, I came to know that sympathy is politically guided, particularly when it comes to Black bodies.

In regards to the Ethiopian civil war, what I experienced when I went home to the heat of the war last year was an entirely different narrative to what was portrayed in Western media. From the position of having my own first-hand accounts, I still find it difficult to piece together the stories behind the political turmoil amidst all the propaganda that was being spread. I took the STS research thesis as an opportunity to take my experience and knowledge in Data and Computer Science to bridge it with my technical capstone work in Data Analytics to explore how Data Feminism could be applied to the Ethiopian civil war.

As we gear ourselves to a data-driven world, whether it be politically motivated or related to a corporate setting, it is essential to place value in unearthing factors that can tamper with representative data. In summation of my work this year, I achieved what I set out to do in terms of understanding the power of data. The greatest takeaway from my work is that no matter one's nationality, status, or identity we all deserve a chance for our truth to be told.

I would like to extend my gratitude to my capstone advisors Sean Ferguson and Rosanne Vrugtman who have assisted me in the process of refining my research and presenting it in a professional manner

BS (Bachelor of Science)
Africa, Ethiopia, Data Feminism
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