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

Robin Quériaux, Solveig Bachellery, Léa Déléris, Saurabhsingh Rajput, Mariam Barry, Tushar Sharma, Jérôme Lebecq · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31588 · cited by 0 works (OpenAlex) · open-access PDF

Hindi

डैशप्लिकेशन संस्थाओं जो विनियमित सेटिंग्स में बड़े भाषा मॉडलों को तैनात करते हैं, उन्हें एक तैनाती को मंजूरी देने से पहले जिम्मेदार एआई प्रथाओं के तकनीकी सबूत की आवश्यकता होती है। COMPL-AI एक यूरोपीय संघ के एआई अधिनियम का तकनीकी व्याख्या प्रदान करता है जो छह सिद्धांत समूहों और 18 तकनीकी आवश्यकताओं के तहत इस सबूत को व्यवस्थित करता है, जिनमें से 12 वर्तमान बेंचमार्क माप सकते हैं। बेंचमार्क सेट जो इस सबूत का उत्पादन करते हैं, आमतौर पर एक कमांड लाइन से संचालित होते हैं, जबकि तैनाती को मंजूरी देने वाले कर्मचारियों को सार्वजनिक इंजीनियर नहीं होते हैं। हम VERA को प्रस्तुत करते हैं, एक खुले स्रोत के स्वयं हो

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Dashboard, Benchmark (surveying), Raw data, Computer science, Benchmarking, Measure (data warehouse)

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