# Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain arXiv: https://arxiv.org/abs/2406.14758 ยท HTML: https://mlsanity.com/paper/2406.14758/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 79 references that are in the map. - First posted on arXiv 20 Jun 2024. ## Builds on (most cited first, 30 of 30) - [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021) - [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020) - [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018) - [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018) - [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021) - [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024) - [Foundational Challenges in Assuring Alignment and Safety of Large Language Models](https://mlsanity.com/paper/2404.09932/) (2024) - [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020) - [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018) - [OpenELM: An Efficient Language Model Family with Open Training and Inference Framework](https://mlsanity.com/paper/2404.14619/) (2024) - [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021) - [Dataset search: a survey](https://mlsanity.com/paper/1901.00735/) (2019) - [Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face](https://mlsanity.com/paper/2401.13822/) (2024) - [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022) - [Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation](https://mlsanity.com/paper/2010.11411/) (2020) - [The Brussels Effect and Artificial Intelligence: How EU regulation will impact the global AI market](https://mlsanity.com/paper/2208.12645/) (2022) - [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024) - [Verifiable evaluations of machine learning models using zkSNARKs](https://mlsanity.com/paper/2402.02675/) (2024) - [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023) - [Federated Learning Priorities Under the European Union Artificial Intelligence Act](https://mlsanity.com/paper/2402.05968/) (2024) - [Helix: Holistic Optimization for Accelerating Iterative Machine Learning](https://mlsanity.com/paper/1812.05762/) (2018) - [What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims](https://mlsanity.com/paper/2406.08205/) (2024) - [On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps](https://mlsanity.com/paper/2202.03541/) (2022) - [Golden Grain: Building a Secure and Decentralized Model Marketplace for MLaaS](https://mlsanity.com/paper/2011.06458/) (2020) - [Prescriptive and Descriptive Approaches to Machine-Learning Transparency](https://mlsanity.com/paper/2204.13582/) (2022) - [Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments](https://mlsanity.com/paper/2312.06153/) (2023) - [Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development](https://mlsanity.com/paper/2307.11525/) (2023) - [Evaluating a Methodology for Increasing AI Transparency: A Case Study](https://mlsanity.com/paper/2201.13224/) (2022) - [Quantifying Transparency of Machine Learning Systems through Analysis of Contributions](https://mlsanity.com/paper/1907.03483/) (2019) - [Explainability in JupyterLab and Beyond: Interactive XAI Systems for Integrated and Collaborative Workflows](https://mlsanity.com/paper/2404.02081/) (2024) ## Built on by (most cited first, 3 of 3) - [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025) - [Computational Compliance for AI Regulation: Blueprint for a New Research Domain](https://mlsanity.com/paper/2601.04474/) (2026) - [An Empirical Study and Assessment of EU AI Act Compliance Checkers](https://mlsanity.com/paper/2609.36228/) (2026) ## Abstract (arXiv) As the AI supply chain grows more complex, AI systems and models are increasingly likely to incorporate multiple internally- or externally-sourced components such as datasets and (pre-trained) models. In such cases, determining whether or not the aggregate AI system or model complies with the EU AI Act (AIA) requires a multi-step process in which compliance-related information about both the AI system or model and all its component parts is: (1) gathered, potentially from multiple arms-length sources; (2) harmonized, if necessary; (3) inputted into an analysis that looks across all of it to render a compliance prediction. Because this process is so complex and time-consuming, it threatens to overburden the limited compliance resources of the AI providers (i.e., developers) who bear much of the responsibility for complying with the AIA. It also renders rapid or real-time compliance analyses infeasible in many AI development scenarios where they would be beneficial to providers. To address these shortcomings, we introduce a complete system for automating provider-side AIA compliance analyses amidst a complex AI supply chain. This system has two key elements. First is an interlocking set of computational, multi-stakeholder transparency artifacts that capture AIA-specific metadata about both: (1) the provider's overall AI system or model; and (2) the datasets and pre-trained models it incorporates as components. Second is an algorithm that operates across all those artifacts to render a real-time prediction about whether or not the aggregate AI system or model complies with the AIA. All told, this system promises to dramatically facilitate and democratize provider-side AIA compliance analyses (and, perhaps by extension, provider-side AIA compliance).