Despite recent theories that algorithms ‘filter out’ users with marginalized identities, identity, Black users are understudied in algorithmic identity research, particularly in how they navigate personalized, algorithmically mediated environments like TikTok. I address this gap by exploring the complex interplay between racial identity, user engagement, and algorithmic bias on TikTok's For You Page (FYP). This is a mixed methods dissertation consisting of three papers, each examining the relationship between racial identity and the FYP algorithm from distinct perspectives. The first paper investigates Black users' folk theories about how the algorithm interprets their racial identity and influences their engagement with the platform. Using Critical Techno-Cultural Discourse Analysis (CTDA), it uncovers how users perceive and respond to algorithmic biases, providing foundational insights into Black users' navigation of algorithmically mediated environments. The second paper highlights how racial identity centrality shapes perceptions of algorithm responsiveness, showing that users who view their racial identity as central tend to interpret the FYP algorithm as both responsive and insensitive. The third paper introduces the concept of algorithmic dissonance, synthesizing findings from both CTDA and survey research to capture the conflicting emotions and beliefs Black users experience in relation to the FYP algorithm's racial bias. Together, these papers advance the study of algorithmic identity co-production and critical digital studies by demonstrating how Black users navigate, interpret, and resist algorithmic power, illuminating the complex interplay between structural biases, user agency, and self-concept within the algorithmic space.