The burgeoning field of algorithmic design, while promising transformative efficiencies, is increasingly scrutinized for its inherent potential to perpetuate, and even amplify, existing societal biases. What is often overlooked in the celebratory discourse surrounding AI, however, is the fundamental epistemological problem at its core. It is not merely a question of poorly trained models or incomplete datasets; rather, algorithmic bias represents a systemic failure in how we conceive of knowledge itself. The very act of encoding human understanding, which is inherently subjective and historically contingent, into ostensibly objective computational structures inevitably imports biases. These biases, furthermore, become obscured by the perceived neutrality of the algorithm, a dangerous illusion of objectivity that masks the underlying value judgments. To mitigate this, a radical re-evaluation of algorithmic development is necessary. Central to this revised approach should be a recognition that data, far from being an objective representation of reality, is invariably shaped by the socio-political context in which it is collected. Consideration must be given not only to the statistical properties of datasets, but also to the historical forces that have shaped their composition and interpretation. Absent such a critical, reflexive engagement with the epistemology of data, the promise of algorithmic progress risks becoming yet another instrument for reinforcing existing inequalities, thereby undermining the very principles of fairness and justice it purports to uphold. Indeed, the consequences of neglecting this epistemological imperative are far-reaching, threatening the integrity of not only our technological systems, but also the social fabric they increasingly govern.