mlsanityData to 14 Oct 2026

Use case cards: a use case reporting framework inspired by the European AI Act

arXiv 2306.13701 ↗First posted 23 Jun 2023cs.CY cs.AIcited by 15

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Abstract (arXiv)

Despite recent efforts by the Artificial Intelligence (AI) community to move towards standardised procedures for documenting models, methods, systems or datasets, there is currently no methodology focused on use cases aligned with the risk-based approach of the European AI Act (AI Act). In this paper, we propose a new framework for the documentation of use cases, that we call "use case cards", based on the use case modelling included in the Unified Markup Language (UML) standard. Unlike other documentation methodologies, we focus on the intended purpose and operational use of an AI system. It consists of two main parts. Firstly, a UML-based template, tailored to allow implicitly assessing the risk level of the AI system and defining relevant requirements. Secondly, a supporting UML diagram designed to provide information about the system-user interactions and relationships. The proposed framework is the result of a co-design process involving a relevant team of EU policy experts and scientists. We have validated our proposal with 11 experts with different backgrounds and a reasonable knowledge of the AI Act as a prerequisite. We provide the 5 "use case cards" used in the co-design and validation process. "Use case cards" allows framing and contextualising use cases in an effective way, and we hope this methodology can be a useful tool for policy makers and providers for documenting use cases, assessing the risk level, adapting the different requirements and building a catalogue of existing usages of AI.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 8 of its 71 references that are in the map. First posted on arXiv 23 Jun 2023.

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8 of 71 references.

References · 8

  1. 2018Datasheets for Datasetscited by 1,386
  2. 2018Model Cards for Model Reportingcited by 1,080
  3. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 325
  4. 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 171
  5. 2022The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligencecited by 26
  6. 2023AI Usage Cards: Responsibly Reporting AI-generated Contentcited by 16
  7. 2022Prescriptive and Descriptive Approaches to Machine-Learning Transparencycited by 7
  8. 2022Documenting use cases in the affective computing domain using Unified Modeling Languagecited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 5; 2026: 3.

15 papers.

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