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Justice After Identity: Large Language Models and the View from Everywhere — arXiv:2610.09053

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.09053
License (as archived): CC0-1.0
Archived: 2026-10-08T11:06:50.022Z
SHA-256 of the archived text: 0b6ec5be4272338bed5b8a8c98767a7aaa0e2f92da64333da81848ce1164f049
Stable URL of this page: https://metraipolis.com/documento/dc_720eeaa064ba

How to cite

Justice After Identity: Large Language Models and the View from Everywhere — arXiv:2610.09053 — arXiv (en), https://arxiv.org/abs/2610.09053 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_720eeaa064ba (Metraipolis archive dc_720eeaa064ba, sha256 0b6ec5be4272338bed5b8a8c98767a7aaa0e2f92da64333da81848ce1164f049)

BibTeX

@misc{dc720eeaa064ba,
  title = {Justice After Identity: Large Language Models and the View from Everywhere — arXiv:2610.09053},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.09053},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_720eeaa064ba; sha256 0b6ec5be4272338bed5b8a8c98767a7aaa0e2f92da64333da81848ce1164f049},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.09053. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The search for a common view of justice and fairness has challenged human collective activity, as our diverging judgments are unavoidably shaped by the self-interests of social position, personal benefit, cultural inheritance, and historical circumstance. John Rawls famously attempted to overcome this limitation through popularizing a philosophical tradition known by the phrase "the original position" - a thought experiment by which people select principles of justice without knowing the identities or advantages they will possess. Critics, however, have long questioned whether people can meaningfully suspend their social identities and suppress morally relevant forms of lived experience. Artificial intelligence engaged to calculate algorithmic and agentic fairness introduces a novel possibility. LLMs have no singular class, race, gender, nationality, or biography, yet their parameters encode linguistic representations of a vast range of human identities and moral traditions. Perhaps the ethical judgments of LLMs could approximate an integrative original position - a "view from everywhere" generated not by excluding social identities but by computationally incorporating their this http URL is unlikely that humankind will "hand over the keys" to computational systems by simply delegating complete agentic control of distributive and procedural collective processes. But AI may play a role, perhaps a positive one, interacting with individual and collective human judgment as we often confront increasingly polarized views on what is fair and just. We present data comparing human, base model, and frontier/fine-tuned model judgments about classic moral dilemmas while systematically varying identity relationships and Rawlsian constraints on identity. We conclude by speculating whether, if advanced AI systems provide humans with thoughtful advice, humans would actually be likely to accept it.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.09053 — 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 2026-10-08T10:14:05.897Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).