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Unveiling Racial Biases in Artificial Intelligence


There are unquestionably many advantages that have been brought about by incorporating artificial intelligence (AI) into many facets of our existence. However, there is a rising worry over the negative aspects of artificial intelligence, particularly concerning the perpetuation and amplification of racial prejudices (Ferrara, 2024). In my next article, I will investigate the complex web of prejudice and bias that exists throughout artificial intelligence systems, with a significant emphasis on racism to be found. To investigate the study topic, this article aims to investigate how racial biases in artificial intelligence systems contribute to and perpetuate racism in society.

Research Question

This work is being driven by a core issue vital for understanding the intricate ways in which artificial intelligence, which is supposed to be neutral and objective, accidentally mimics and amplifies existing racial prejudices. I plan to reveal how artificial intelligence becomes a conduit for racial biases by examining the algorithms, databases, and decision-making processes. The study will not only acknowledge the presence of prejudice but also investigate how various forms of bias present themselves in artificial intelligence technology.

Significance of the Argument

This argument is significant because it clarifies biased AI systems’ practical effects. Racial prejudice must be recognized and mitigated as artificial intelligence (AI) is progressively incorporated into decision-making processes, from employment to criminal justice. The article will add to the current technology and social justice conversation by discussing how biased AI affects underprivileged groups. By empowering people with information and awareness, our initiative aims to promote a more informed conversation regarding the ethical implications of artificial intelligence.

Target Audience

This article is intended for many readers, including the general public, educators, technologists, and policymakers. Using these findings, policymakers may create policies that support AI systems’ accountability and justice (Varsha, 2023). Technologists will promote responsible innovation by developing a greater understanding of the ethical issues surrounding AI research. Educators may incorporate these results into the curriculum to better educate students about the ethical dilemmas rapidly expanding technology will bring. At last, the public will possess the necessary information to participate in conversations about the effects of biased AI on society.

Supporting Source

Distinguished Investigator and Senior Associate Investigator at Microsoft’s Research Department Kate Crawford will be cited in my argument (Ferrara, 2024). By ignoring fundamental biases in massive amounts of data and learning algorithms, Crawford argues in her article “The Hidden Prejudices in Big Data,” which describes the unforeseen results of such approaches. Crawford’s study sheds light on how artificial intelligence (AI) systems might worsen and maintain existing social disparities if not regulated and overseen with caution.


When I finish my next article, I will be doing an inquiry on the prevalence of racial biases in artificial intelligence systems, with a particular emphasis on this phenomenon’s implications for society. I will be focusing on the consequences that this phenomenon has for society. The purpose of this article is to contribute to a future that is more ethical and fair in the field of artificial intelligence by dissecting the complicated connection between technology and social dynamics. This will be accomplished by analyzing the relationship between the two. Analyzing the intricate relationship between the two will be how this objective will be realized.


Ferrara, E. (2024). The butterfly effect in artificial intelligence systems: implications for AI bias and fairness. Machine Learning with Applications, p. 15, 100525.

Varsha, P. S. (2023). How can we manage biases in artificial intelligence systems–A systematic literature review. International Journal of Information Management Data Insights3(1), 100165.


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