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Intersectionality and reflexivity—decolonizing methodologies for the data science process

23Strands News
23Strands News December 10, 2021

Using intersectionality as a methodology illuminated the shortcomings of the data science process when analyzing the viral #metoo movement

Intersectionality and reflexivity—decolonizing methodologies for the data science process

Intersectionality in data science refers to an analytical framework for understanding how aspects of a person's social and political identities combine to create different modes of discrimination and privilege.

As a company that uses data science techniques and artificial intelligence, it is really important for us as a team at 23Strands to raise awareness and highlight discussions about intersectionality in the field. As stated in the article "The key is to implement intersectionality to its fullest potential, to expose nuances and inequities, alter our approaches from the standard perfunctory tasks, reflect how we aid and abide by systems and structures of power, and begin to break the habit of recolonizing ourselves as data scientists."

So how can data science be more inclusive when using methodologies that promote linear thinking on complex fluid populations? People and societies are complex, dynamic, and fluid. Yet, we use linear formulaic processes to generate data reducing them into generic, flattened results, distorting views of populations in the process. Read below to find out more.