Algorithmic bias, an inherent problem in automated systems, significantly impacts how individuals consume online news. News aggregators and social media platforms utilize complex algorithms to personalize content feeds, ostensibly tailoring information to individual preferences. However, these algorithms often learn from historical data that reflects existing societal biases, inadvertently perpetuating and even amplifying these biases in the news presented to users. This can lead to the creation of 'filter bubbles,' where individuals are primarily exposed to viewpoints that confirm their pre-existing beliefs, thereby limiting their exposure to diverse perspectives and potentially reinforcing societal polarization. The consequences of algorithmic bias in news consumption are far-reaching. Firstly, it undermines the principle of informed citizenship, as individuals may not receive a balanced representation of current events. Secondly, it can exacerbate existing inequalities, as biased algorithms may systematically disadvantage certain demographic groups by either suppressing their voices or promoting negative stereotypes. Finally, the subtle yet pervasive nature of algorithmic bias makes it difficult for individuals to recognize and counteract its effects, posing a considerable challenge to media literacy and critical thinking skills.