The proliferation of algorithmic systems across diverse societal sectors has instigated a fervent debate regarding their purported objectivity. Central to this discourse is the assertion that algorithms, being devoid of human biases, furnish impartial and equitable outcomes. However, a more nuanced examination reveals the inherent limitations in this assumption. What is often overlooked is that algorithms, irrespective of their mathematical sophistication, are invariably products of human design, reflecting the values, priorities, and, crucially, the biases of their creators. Consequently, the data sets employed to train these algorithms, frequently drawn from historical records, may perpetuate or even amplify existing societal inequalities, thus undermining the very notion of algorithmic neutrality. Further complicating matters is the inherent opacity of many complex algorithms, particularly those employing deep learning techniques. This “black box” nature renders it challenging, if not impossible, to ascertain the precise mechanisms driving algorithmic decision-making. Thus, even when unintended biases manifest in algorithmic outputs, their sources may remain obscured, precluding effective remediation. It is, therefore, imperative to approach claims of algorithmic objectivity with a healthy dose of skepticism, recognizing that algorithms are not objective entities in and of themselves, but rather sophisticated tools whose outcomes are inextricably linked to the human contexts in which they are conceived, developed, and deployed. To mitigate the risks associated with biased algorithms, emphasis should be placed on promoting transparency, accountability, and rigorous auditing practices to ensure fairness and equity.