Ehsan, Samia2025-05-242025-05-242025-05-23vt_gsexam:43849https://hdl.handle.net/10919/134206Artificial intelligence (AI) systems, particularly recommendation algorithms, have transformed user engagement on digital platforms like Netflix and YouTube by delivering personalized experiences. However, biases in data, algorithms, and deployment environments raise significant social and professional concerns. This thesis uses Netflix and YouTube as case examples to study the relationship between AI bias and corporate reputation. The study examines how these companies utilize AI recommendation systems and whether biases are presented, and the reputational risks they might pose. The thesis employs thematic analysis to analyze news media coverage from Google News and from major technology news outlets between January 2017 to March 2025. The qualitative data analysis software NVivo has been used in this thesis to code and identify recurring themes systematically. This thesis examines various forms of bias in AI recommendation systems—including racism, sexism, and cultural misrepresentation—alongside the severity of algorithmic discrimination and the impact of corporate reputational risks these biases create.ETDenIn CopyrightMedia narrativePlatform governanceDigital trustAI perceptionCorporate communicationAlgorithmic accountabilityThesis