Can the United States' Google Search Algorithm be Reflective of Implicit Racial Bias?

Abstract

Given the sociological consensus of systemic and implicit racial bias in many American institutions, the interactions of those systems and their sources with computer science and algorithms, specifically Google Search, are largely unknown. The current literature aiming to research the relationship between implicit bias and computer algorithms, such as “Algorithms of Oppression: How Search Engines Reinforce Racism” by Dr. Safiya Noble and “Race After Technology: Abolitionist Tools for the New Jim Code” by Professor Ruha Benjamin, seems to demonstrate that there is evidence or precedence of computer algorithms having implicit racial bias. Given the current state of the literature on this topic, this study aims to demonstrate whether the United States’ Google Search algorithm is reflective of implicit racial bias.
 
 With the hypothesis that the United States’ Google Search algorithm is reflective of implicit racial bias, in order to test this hypothesis, the method would require the collection of data, mean search times, the organization of data into normal distributions, and the use of statistical analysis, hypothesis testing, to determine an algorithmic bias. The results suggest that with specific occupational search queries, Google’s search algorithm seems to have faster mean search times for white occupational search queries than racial minority occupational search queries and tend to be significantly different from occupational search queries and White occupational search queries. Given the evidence performed by hypothesis testing, this research paper assertively concludes that the United States’ Google search algorithm is reflective of implicit racial bias.