QUANTIFYING ALGORITHMIC BIAS IN NEWS RECOMMENDATIONS: METHODOLOGIES AND CASE STUDIES

Abstract

This study investigates algorithmic bias in news recommendations, a critical issue in today’s digital media landscape. As recommendation algorithms curate personalized content, they can also perpetuate systematic biases that distort information access and public discourse. The research begins with a literature review, identifying key themes and gaps in understanding algorithmic bias. A robust methodology is developed, incorporating user-centric analyses, content diversity assessments, and fairness evaluations to quantify the impact of bias in news recommendations. Through detailed case studies, the study highlights how biased algorithms shape user experiences, limit exposure to diverse perspectives, and contribute to societal polarization. The findings emphasize the urgent need for ethical considerations in algorithm design and provide actionable recommendations for media organizations, technology companies, and policymakers. By advocating for transparency, accountability, and user empowerment, this research aims to foster a more equitable digital information environment. Ultimately, the study contributes to the discourse on algorithmic bias, promoting a media landscape where diverse voices are heard and the integrity of journalism is maintained in the age of personalization.