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LLM Recommenders Produce Demographic Inequality Through Content Preferences — arXiv:2610.41854

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41854
License (as archived): CC0-1.0
Archived: 2026-11-03T07:48:27.500Z
SHA-256 of the archived text: 49c2d0d1b74cb96ac4d146298fe697a8332b09d00c6917639573e0b5d64790a3
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How to cite

LLM Recommenders Produce Demographic Inequality Through Content Preferences — arXiv:2610.41854 — arXiv (en), https://arxiv.org/abs/2610.41854 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1be2fdf2feee (Metraipolis archive dc_1be2fdf2feee, sha256 49c2d0d1b74cb96ac4d146298fe697a8332b09d00c6917639573e0b5d64790a3)

BibTeX

@misc{dc1be2fdf2feee,
  title = {LLM Recommenders Produce Demographic Inequality Through Content Preferences — arXiv:2610.41854},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41854},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1be2fdf2feee; sha256 49c2d0d1b74cb96ac4d146298fe697a8332b09d00c6917639573e0b5d64790a3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41854. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: governance digitale e beni comuni (elenco arXiv cs.CY, nuovi lavori del giorno).
Categorie arXiv: Computers and Society (cs.CY) ; Social and Information Networks (cs.SI).
Pagina del lavoro: https://arxiv.org/abs/2610.41854 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:40:52.172Z dalla pagina https://arxiv.org/list/cs.CY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).