Algorithmic bias in content curation and recommendation algorithms has become a significant concern in the digital age, shaping the information and experiences users encounter online. This article delves into the intricate web of biases that permeate algorithms, influencing the content users see and the recommendations they receive. By exploring the impact of algorithmic bias on content curation, this discussion aims to shed light on the implications, root causes, and potential solutions to address the inherent biases embedded in recommendation systems.
