Ethical COVID-19 Detection via Machine Learning: An Unblemished Approach

Abstract

The sudden appearance of the highly lethal COVID-19 virus, also known as SARS-CoV-2, has ignited a worldwide emergency of unparalleled proportions, continuously affecting the daily existence of a vast multitude of people. Beyond its devastating health consequences, this pandemic has unleashed profound repercussions on a global scale, inducing negative GDP trends, economic downturns, elevated mortality rates, and multifaceted societal challenges. Consequently, research endeavors spanning the globe have been ignited, delving into diverse aspects of the virus, encompassing vaccine development, and diagnostic methodologies. Within the scope of this paper, we delve into the exploration of methodologies for identifying COVID-19 afflicted individuals. We propose a dual-pronged approach to discerning the presence of the virus in individuals. This research paper delves into the ethical dimensions of COVID-19 detection through machine learning, emphasizing an unblemished approach that upholds integrity, transparency, and fairness. Our investigation explores critical facets such as data privacy, fairness, transparency, and accountability. We advocate for an approach that not only prioritizes the accuracy of detection but also safeguards individual rights and fosters trust among healthcare professionals and the wider public. This paper outlines the essential principles and practices for ethically sound COVID-19 detection, guiding the way for responsible and effective utilization of machine learning in the ongoing battle against the pandemic.

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