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

arXiv: https://arxiv.org/abs/2306.13701 · HTML: https://mlsanity.com/paper/2306.13701/

## 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 (most cited first, 8 of 8)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022)
- [AI Usage Cards: Responsibly Reporting AI-generated Content](https://mlsanity.com/paper/2303.03886/) (2023)
- [Prescriptive and Descriptive Approaches to Machine-Learning Transparency](https://mlsanity.com/paper/2204.13582/) (2022)
- [Documenting use cases in the affective computing domain using Unified Modeling Language](https://mlsanity.com/paper/2209.09666/) (2022)

## Built on by (most cited first, 15 of 15)

- [AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act](https://mlsanity.com/paper/2406.18211/) (2024)
- [Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts](https://mlsanity.com/paper/2407.17374/) (2024)
- [Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain](https://mlsanity.com/paper/2406.14758/) (2024)
- [TechOps: Technical Documentation Templates for the AI Act](https://mlsanity.com/paper/2508.08804/) (2025)
- [Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework](https://mlsanity.com/paper/2501.08046/) (2025)
- [Justified Evidence Collection for Argument-based AI Fairness Assurance](https://mlsanity.com/paper/2505.08064/) (2025)
- [CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems](https://mlsanity.com/paper/2508.19281/) (2025)
- [PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation](https://mlsanity.com/paper/2601.11702/) (2026)
- [Fine-Grained Traceability for Transparent ML Pipelines](https://mlsanity.com/paper/2601.14971/) (2026)
- [Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems](https://mlsanity.com/paper/2307.13658/) (2023)
- [Documentation Practices of Artificial Intelligence](https://mlsanity.com/paper/2406.18620/) (2024)
- [Leveraging Ontologies to Document Bias in Data](https://mlsanity.com/paper/2407.00509/) (2024)
- [AICat: An AI Cataloguing Approach to Support the EU AI Act](https://mlsanity.com/paper/2501.04014/) (2024)
- [From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care](https://mlsanity.com/paper/2506.13584/) (2025)
- [PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models](https://mlsanity.com/paper/2610.16980/) (2026)

## 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.