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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Anthony Victor Onwuegbuzia
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DOI:10.17265/2328-2134/2024.01.005
California State University Dominguez Hills, Carson, the United States
This paper delves into the intricate interplay between artificial intelligence (AI) systems and the perpetuation of Anti-Black racism within the United States medical industry. Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities, there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities. Focusing specifically on the experiences of Black patients, this research investigates how the following AI components: medical algorithms, machine learning, and natural learning processes are contributing to the unequal distribution of medical resources, diagnosis, and health care treatment of those classified as Black. Furthermore, this review employs a multidisciplinary approach, combining insights from computer science, medical ethics, and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases. By dissecting the three primary components of AI, this paper aims to present a clear understanding of how these technologies work, how they intersect, and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients. Furthermore, this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency, accountability, and diversity in the development and implementation of these technologies. Finally, it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text. Therefore, I assert the following: Anti-Black racism refers to prejudice, discrimination, or antagonism directed against individuals or communities of African descent based on their race. It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people. Furthermore, I proclaim that this form of racism can be manifested in various ways, such as unequal access to opportunities, resources, education, employment, and fair treatment within social, economic, and political systems. It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S. borders and beyond, leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities. Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality. Efforts to combat Anti-Black racism include promoting awareness, education, advocacy for policy changes, and fostering a culture of inclusivity and equality.
Bias in algorithms, Racial disparities in U.S. healthcare, Discriminatory healthcare practices, Black patient outcomes, Automated decision-making and racism, Machine Learning, Natural language processing
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