arXiv cs.CY· Robin Qu\'eriaux, Solveig Bachellery, L\'ea D\'el\'eris, Saurabhsingh Rajput, Mariam Barry, Tushar Sharma, J\'er\^ome Lebecq·· 13 小时前AI 评分31
VERA:面向 EU AI Act 合规评估的开源 LLM 部署责任 AI 仪表盘
VERA: A Responsible AI Dashboard for Assessing LLM Deployments in Light of the EU AI Act
AI 导读
VERA 是一款开源自托管仪表盘,用于按 EU AI Act 的 12 项可测量技术要求对 LLM 部署进行评分,并记录每次评测的能耗。在一家欧洲大型银行的受试者研究中,使用 VERA 完成责任 AI 评估平均耗时从 56 秒降至 30 秒,组织内问卷有用性评分为 4.36/5。仪表盘与研究材料已公开,以支持在不同组织环境中复现。
正文 · 原文
Abstract: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.
| Comments: | Accepted at the 33rd Asia-Pacific Software Engineering Conference (APSEC 2026), Software Engineering in Practice track, Bali, Indonesia, 7-10 December 2026. 11 pages, 3 figures |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.31588 [cs.SE] |
| (or arXiv:2610.31588v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31588 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Robin Quériaux [view email]
[v1]
Thu, 22 Oct 2026 17:23:39 UTC (5,553 KB)
来源:arXiv cs.CY · arxiv.org