# AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act

arXiv: https://arxiv.org/abs/2406.18211 · HTML: https://mlsanity.com/paper/2406.18211/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Demystifying the Draft EU Artificial Intelligence Act](https://mlsanity.com/paper/2107.03721/) (2021)
- [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022)
- [Risk management in the Artificial Intelligence Act](https://mlsanity.com/paper/2212.03109/) (2022)
- [The Transformative Potential of Artificial Intelligence](https://mlsanity.com/paper/1912.00747/) (2019)
- [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023)
- [Stronger Together: on the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML](https://mlsanity.com/paper/2305.18615/) (2023)
- [Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments](https://mlsanity.com/paper/2312.06153/) (2023)
- [Data Privacy Vocabulary (DPV) -- Version 2](https://mlsanity.com/paper/2404.13426/) (2024)

## Built on by (most cited first, 10 of 10)

- [STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports](https://mlsanity.com/paper/2508.09853/) (2025)
- [A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2410.17281/) (2024)
- [TechOps: Technical Documentation Templates for the AI Act](https://mlsanity.com/paper/2508.08804/) (2025)
- [The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms](https://mlsanity.com/paper/2508.16672/) (2025)
- [Developing an Ontology for AI Act Fundamental Rights Impact Assessments](https://mlsanity.com/paper/2501.10391/) (2024)
- [Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond](https://mlsanity.com/paper/2507.08866/) (2025)
- [AICat: An AI Cataloguing Approach to Support the EU AI Act](https://mlsanity.com/paper/2501.04014/) (2024)
- [RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems](https://mlsanity.com/paper/2603.28735/) (2026)
- [Governance-as-Code: Translating EU AI Act Technical Requirements into Executable Compliance Pipelines for Generative AI Systems](https://mlsanity.com/paper/2609.20016/) (2026)
- [An Empirical Study and Assessment of EU AI Act Compliance Checkers](https://mlsanity.com/paper/2609.36228/) (2026)

## Abstract (arXiv)

With the upcoming enforcement of the EU AI Act, documentation of high-risk AI systems and their risk management information will become a legal requirement playing a pivotal role in demonstration of compliance. Despite its importance, there is a lack of standards and guidelines to assist with drawing up AI and risk documentation aligned with the AI Act. This paper aims to address this gap by providing an in-depth analysis of the AI Act's provisions regarding technical documentation, wherein we particularly focus on AI risk management. On the basis of this analysis, we propose AI Cards as a novel holistic framework for representing a given intended use of an AI system by encompassing information regarding technical specifications, context of use, and risk management, both in human- and machine-readable formats. While the human-readable representation of AI Cards provides AI stakeholders with a transparent and comprehensible overview of the AI use case, its machine-readable specification leverages on state of the art Semantic Web technologies to embody the interoperability needed for exchanging documentation within the AI value chain. This brings the flexibility required for reflecting changes applied to the AI system and its context, provides the scalability needed to accommodate potential amendments to legal requirements, and enables development of automated tools to assist with legal compliance and conformity assessment tasks. To solidify the benefits, we provide an exemplar AI Card for an AI-based student proctoring system and further discuss its potential applications within and beyond the context of the AI Act.