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ALTO: African and Levantine Tech-Facilitated Gender-Based Violence Corpus — arXiv:2611.06706

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.06706
License (as archived): CC0-1.0
Archived: 2026-11-05T18:18:04.833Z
SHA-256 of the archived text: dbbf0066bd860782707ffca482734af563a95a28cc1831c65be704bb5431ed35
Stable URL of this page: https://metraipolis.com/documento/dc_efee70d04722

How to cite

ALTO: African and Levantine Tech-Facilitated Gender-Based Violence Corpus — arXiv:2611.06706 — arXiv (en), https://arxiv.org/abs/2611.06706 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_efee70d04722 (Metraipolis archive dc_efee70d04722, sha256 dbbf0066bd860782707ffca482734af563a95a28cc1831c65be704bb5431ed35)

BibTeX

@misc{dcefee70d04722,
  title = {ALTO: African and Levantine Tech-Facilitated Gender-Based Violence Corpus — arXiv:2611.06706},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.06706},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_efee70d04722; sha256 dbbf0066bd860782707ffca482734af563a95a28cc1831c65be704bb5431ed35},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.06706. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Tech-facilitated gender based violence (TFGBV) is a pervasive online harm affecting women and gender minority groups through its many direct and covert forms. Current models and annotated datasets for gendered harm detection are dominated by data from, and models trained in, Global North languages. In collaboration with civil society organizations (CSOs) from Jordan, Francophone West Africa, and Kenya, we developed a community-led approach to crowdsource culturally and linguistically relevant instances of TFGBV in Levantine Arabic, African French, and Swahili. We annotated the crowdsourced data using active learning to develop a small but performant dataset (N=3022) called ALTO to improve TFGBV detection in the three languages. Through benchmarking and fine-tuning experiments, we show that existing harmful content detection models show poor TFGBV recall within ALTO and improve with fine-tuning. We found that models often miss covert harms across all languages, while error rates are markedly higher in Swahili. We report the experimental results and release the annotation taxonomy, dataset, and trained models.

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); Computers and Society (cs.CY); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.06706 — 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 2026-11-05T18:12:49.064Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).