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Lighthouse: On Building a Trustworthy Digital Assistant for Exploring History with Civil Rights Archives — arXiv:2610.30158

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.30158
License (as archived): CC0-1.0
Archived: 2026-10-25T20:54:31.163Z
SHA-256 of the archived text: d6907e13b67392b95884c32f33b4a11c8254d599ac6a5a6fa930f1cd53f8629c
Stable URL of this page: https://metraipolis.com/documento/dc_7b818bf63b4e

How to cite

Lighthouse: On Building a Trustworthy Digital Assistant for Exploring History with Civil Rights Archives — arXiv:2610.30158 — arXiv (en), https://arxiv.org/abs/2610.30158 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7b818bf63b4e (Metraipolis archive dc_7b818bf63b4e, sha256 d6907e13b67392b95884c32f33b4a11c8254d599ac6a5a6fa930f1cd53f8629c)

BibTeX

@misc{dc7b818bf63b4e,
  title = {Lighthouse: On Building a Trustworthy Digital Assistant for Exploring History with Civil Rights Archives — arXiv:2610.30158},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.30158},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7b818bf63b4e; sha256 d6907e13b67392b95884c32f33b4a11c8254d599ac6a5a6fa930f1cd53f8629c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.30158. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Artificial intelligence (AI), as a decision-support aid, provides new tools for studying history effectively using multimodal data, including text, images, and audio. We present, to our knowledge, the first digital assistant named Lighthouse--commonly called a chatbot--for exploring the Civil Rights period of the United States using data from South Carolina. From an AI perspective, the system's novelty lies in its guiding design principles of (a) focusing on trustworthiness of content and (b) by modeling and reducing potential harm to users from generated answers. It implements these with a neuro-symbolic approach that combines neural learning through a large language model (LLM) with rule-based symbolic solving, balancing risk of answers with generality of coverage. We evaluate Lighthouse with both factual objective questions that admit a single acceptable answer as well as subjective questions. For the latter, a dataset of subjective question-answer pairs (QAs) is created by discovering a "gold standard" by aggregating answers from independent experts. Lighthouse has access to over 18k primary sources from a range of collections and can be easily extended.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.30158 — 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 2026-10-25T20:47:56.952Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).