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VERA: A Responsible AI Dashboard for Assessing LLM Deployments in Light of the EU AI Act — arXiv:2610.31588

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.31588
License (as archived): CC0-1.0
Archived: 2026-10-25T08:35:40.790Z
SHA-256 of the archived text: 2dea7c283c26a177f2031d94900672692894fb6f940101246f222d381fe9ec9f
Stable URL of this page: https://metraipolis.com/documento/dc_c52c8a2ac3ef

How to cite

VERA: A Responsible AI Dashboard for Assessing LLM Deployments in Light of the EU AI Act — arXiv:2610.31588 — arXiv (en), https://arxiv.org/abs/2610.31588 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c52c8a2ac3ef (Metraipolis archive dc_c52c8a2ac3ef, sha256 2dea7c283c26a177f2031d94900672692894fb6f940101246f222d381fe9ec9f)

BibTeX

@misc{dcc52c8a2ac3ef,
  title = {VERA: A Responsible AI Dashboard for Assessing LLM Deployments in Light of the EU AI Act — arXiv:2610.31588},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.31588},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c52c8a2ac3ef; sha256 2dea7c283c26a177f2031d94900672692894fb6f940101246f222d381fe9ec9f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.31588. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Institutions that deploy large language models in regulated settings often need technical evidence of responsible AI practices before approving a deployment. COMPL-AI provides a technical interpretation of the EU AI Act that organises this evidence under six principle groups and eighteen technical requirements, twelve of which current benchmarks can measure. The benchmark suites that produce this evidence are typically operated from a command line, while the staff who approve deployments are non-engineers. We present VERA, an open-source self-hosted dashboard deployed to facilitate responsible AI processes within a large European bank. A reviewer selects a model and runs public benchmarks against it. VERA scores the twelve requirements that current benchmarks can measure and instruments the energy consumed by each evaluation, adding run-level evidence for environmental impact. We run four served open-weight models through the requirement pipeline, and the dashboard displays a score for each measurable requirement. A within-subject study then compares how participants read these benchmark results: from the raw output files in one condition, and through the VERA dashboard in the other. The dashboard reduced the time needed to perform the responsible AI assessment, reducing the time required from 56 seconds using raw files to 30 seconds using our dashboard, on average. Our dashboard scored highly (4.36 out of 5) on usefulness in the survey conducted within the organization. The dashboard and the study material are released publicly to promote replicability and reproducibility in different organisational contexts.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2610.31588 — 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 2026-10-25T08:31:13.274Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).