Use case cards: a use case reporting framework inspired by the European AI Act
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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.
Builds on
8 of 71 references.
References · 8
- 2018Datasheets for Datasetscited by 1,386
- 2018Model Cards for Model Reportingcited by 1,080
- 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 325
- 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 171
- 2022The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligencecited by 26
- 2023AI Usage Cards: Responsibly Reporting AI-generated Contentcited by 16
- 2022Prescriptive and Descriptive Approaches to Machine-Learning Transparencycited by 7
- 2022Documenting use cases in the affective computing domain using Unified Modeling Languagecited by 1
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Citing papers per year: 2023: 1; 2024: 6; 2025: 5; 2026: 3.
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- 2024AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Actcited by 10
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Expertscited by 10
- 2024Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chaincited by 3
- 2025TechOps: Technical Documentation Templates for the AI Actcited by 3
- 2025Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Frameworkcited by 2
- 2025Justified Evidence Collection for Argument-based AI Fairness Assurancecited by 2
- 2025CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systemscited by 1
- 2026PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluationcited by 1
- 2026Fine-Grained Traceability for Transparent ML Pipelinescited by 1
- 2023Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systemscited by 0
- 2024Documentation Practices of Artificial Intelligencecited by 0
- 2024Leveraging Ontologies to Document Bias in Datacited by 0
3 more
- 2024AICat: An AI Cataloguing Approach to Support the EU AI Actcited by 0
- 2025From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Carecited by 0
- 2026PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Modelscited by 0
- 2026PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models12 Oct 2026
- 2026Fine-Grained Traceability for Transparent ML Pipelines21 Jan 2026
- 2026PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation16 Jan 2026
- 2025CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems24 Aug 2025
- 2025TechOps: Technical Documentation Templates for the AI Act12 Aug 2025
- 2025From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care16 Jun 2025
- 2025Justified Evidence Collection for Argument-based AI Fairness Assurance12 May 2025
- 2025Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework14 Jan 2025
- 2024AICat: An AI Cataloguing Approach to Support the EU AI Act19 Dec 2024
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts24 Jul 2024
- 2024Leveraging Ontologies to Document Bias in Data29 Jun 2024
- 2024Documentation Practices of Artificial Intelligence26 Jun 2024
3 more
- 2024AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act26 Jun 2024
- 2024Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain20 Jun 2024
- 2023Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems25 Jul 2023
- 2024AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Actcited by 10
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Expertscited by 10
- 2024Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chaincited by 3
- 2025TechOps: Technical Documentation Templates for the AI Actcited by 3
- 2025Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Frameworkcited by 2
- 2025Justified Evidence Collection for Argument-based AI Fairness Assurancecited by 2
- 2025CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systemscited by 1
- 2026PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluationcited by 1
- 2026Fine-Grained Traceability for Transparent ML Pipelinescited by 1
- 2023Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systemscited by 0
- 2024Documentation Practices of Artificial Intelligencecited by 0
- 2024Leveraging Ontologies to Document Bias in Datacited by 0
12 most cited of 15 All 15 (JSON)
- 2026PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models12 Oct 2026
- 2026Fine-Grained Traceability for Transparent ML Pipelines21 Jan 2026
- 2026PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation16 Jan 2026
- 2025CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems24 Aug 2025
- 2025TechOps: Technical Documentation Templates for the AI Act12 Aug 2025
- 2025From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care16 Jun 2025
- 2025Justified Evidence Collection for Argument-based AI Fairness Assurance12 May 2025
- 2025Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework14 Jan 2025
- 2024AICat: An AI Cataloguing Approach to Support the EU AI Act19 Dec 2024
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts24 Jul 2024
- 2024Leveraging Ontologies to Document Bias in Data29 Jun 2024
- 2024Documentation Practices of Artificial Intelligence26 Jun 2024
12 newest of 15 All 15 (JSON)