A. The proliferation of facial recognition technology (FRT) into public spaces represents one of the most significant technological and social shifts of the 21st century. Deployed in airports, city centres, and at public events, proponents advocate for its efficacy in enhancing security, preventing crime, and streamlining services. However, this optimistic narrative is increasingly challenged by a growing body of evidence exposing the technology's profound ethical failings. Beyond a generalised concern for privacy, a more specific and insidious problem lies within the very code of these systems: algorithmic bias. This article will contend that the inherent biases within current FRT applications pose a direct threat to social equity and justice, disproportionately affecting marginalised communities and demanding urgent regulatory scrutiny. B. The origins of algorithmic bias in FRT are not rooted in malicious intent but in a fundamental flaw in machine learning pedagogy: the quality of the training data. For an algorithm to accurately identify a human face, it must first be ‘trained’ on a vast dataset of images. Historically, these datasets have been overwhelmingly populated with im…
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