Humans have always relied on social learning to navigate the social and physical world. But for the first time in history, we are interacting in online social networks where content algorithms filter social information. Little is known about how these algorithms influence our social learning. In this review, we synthesize emerging insights into this ‘algorithm-mediated social learning’ and propose a framework that examines its consequences in terms of functional misalignment. We argue that the functions of human social learning and the goals of content algorithms are misaligned in practice. Algorithms exploit basic human social learning biases (i.e., a bias toward PRestigious, Ingroup, Moral and Emotional information, or PRIME information) as a side effect of their goals to sustain attention and maximize engagement on platforms. Social learning biases function to promote adaptive behaviors that foster cooperation and collective problem-solving. However, when social learning biases are exploited by algorithms, PRIME information saturates the digital social environment in ways that produce social misperceptions that are associated with conflict and misinformation. We show how this problem is ultimately driven by human-algorithm feedback loops where observational and reinforcement learning exacerbate algorithmic amplification, and how it may impact cultural evolution. Finally, we discuss practical solutions for mitigating functional misalignment in human-algorithm interactions via strategies that help algorithms promote more diverse and contextually-sensitive information environments.