The increasing reliance on algorithmic decision-making across diverse sectors, from finance to healthcare, necessitates a critical examination of the concept of algorithmic transparency. While often touted as a panacea for mitigating biases and ensuring accountability, the pursuit of complete algorithmic transparency presents a nuanced paradox. On one hand, exposing the inner workings of algorithms – their parameters, training data, and decision-making processes – empowers individuals and oversight bodies to scrutinize potential discriminatory outcomes. Were such transparency to be implemented universally, skewed outcomes could be readily identified and addressed. On the other hand, complete transparency risks rendering algorithms vulnerable to manipulation and gaming. Sophisticated actors could exploit knowledge of the algorithmic structure to optimize their behavior in ways that undermine the system's intended objectives or unfairly advantage themselves. This is especially pertinent in domains where strategic interaction is prevalent, such as market pricing or resource allocation. Moreover, the very act of exposing sensitive training data may violate privacy regulations and reveal proprietary business strategies, thus stifling innovation. Therefore, rather than striving for absolute transparency, a more pragmatic approach lies in pursuing differential transparency, tailoring the level of disclosure to the specific context and stakeholders involved. Only through careful consideration of these competing interests can we harness the benefits of algorithmic systems while mitigating their inherent risks. Failure to appreciate this paradox risks undermining the very values that transparency is intended to serve.