Large language models (LLMs) are increasingly being integrated into systems that rank and recommend online content. We examine how this shift can produce demographic inequality in whose voices are surfaced. Using 10,000 Twitter/X posts written by 1,000 survey respondents and linked to verified self-reported characteristics across nine demographic attributes, we conduct a controlled recommendation experiment across three LLM providers, six recommendation objectives, and five levels of contextual information. By varying whether models receive post text alone, platform metadata, or explicit author demographics, we isolate three pathways to unequal recommendation: preferences for demographically patterned content, reliance on unequal engagement signals, and direct use of disclosed identity. We find that demographic inequality emerges from text alone. Models consistently favour longer, more negative, and more opinionated posts, particularly those concerning news and social issues, producing systematic differences in the demographic composition of recommended authors. Platform metadata generally amplifies these inequalities, while explicitly disclosing authors' demographic characteristics produces little additional change. Recommendation objectives mainly affect the magnitude of these patterns: controversial framing intensifies both content and demographic skew, whereas popularity-oriented framing dampens it, although for some attributes - most notably age - the objective can also reverse which group is favoured. LLM-based recommenders can therefore reproduce demographic inequality by proxy, not through explicit demographic targeting, but through preferences for content and engagement signals already structured by social inequality.

翻译：暂无翻译