# TechOps: Technical Documentation Templates for the AI Act arXiv: https://arxiv.org/abs/2508.08804 ยท HTML: https://mlsanity.com/paper/2508.08804/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 76 references that are in the map. - First posted on arXiv 12 Aug 2025. ## Builds on (most cited first, 30 of 30) - [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018) - [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018) - [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020) - [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021) - [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022) - [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020) - [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020) - [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022) - [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019) - [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018) - [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022) - [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023) - [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022) - [Data Portraits: Recording Foundation Model Training Data](https://mlsanity.com/paper/2303.03919/) (2023) - [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022) - [Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems](https://mlsanity.com/paper/2101.09385/) (2021) - [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022) - [TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI](https://mlsanity.com/paper/2306.06924/) (2023) - [Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework](https://mlsanity.com/paper/2405.02703/) (2024) - [Data Readiness Report](https://mlsanity.com/paper/2010.07213/) (2020) - [A Methodology for Creating AI FactSheets](https://mlsanity.com/paper/2006.13796/) (2020) - [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023) - [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) - [AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI](https://mlsanity.com/paper/2406.19256/) (2024) - [Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments](https://mlsanity.com/paper/2312.06153/) (2023) - [The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track](https://mlsanity.com/paper/2410.22473/) (2024) - [DAG Card is the new Model Card](https://mlsanity.com/paper/2110.13601/) (2021) - [AI, Climate, and Transparency: Operationalizing and Improving the AI Act](https://mlsanity.com/paper/2409.07471/) (2024) - [Beyond Incompatibility: Trade-offs between Mutually Exclusive Fairness Criteria in Machine Learning and Law](https://mlsanity.com/paper/2212.00469/) (2022) - [Design choice and machine learning model performances](https://mlsanity.com/paper/2201.10239/) (2022) ## Built on by (most cited first, 3 of 3) - [RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems](https://mlsanity.com/paper/2603.28735/) (2026) - [Time-Series Forecasting in Safety-Critical Environments: An Open-Source Package for EU-AI-Act-Compliant Development / Zeitreihenprognose in sicherheitskritischen Umgebungen: Ein Open-Source-Paket f"ur die KI-VO-konforme Entwicklung](https://mlsanity.com/paper/2604.23859/) (2026) - [An Empirical Study and Assessment of EU AI Act Compliance Checkers](https://mlsanity.com/paper/2609.36228/) (2026) ## Abstract (arXiv) Operationalizing the EU AI Act requires clear technical documentation to ensure AI systems are transparent, traceable, and accountable. Existing documentation templates for AI systems do not fully cover the entire AI lifecycle while meeting the technical documentation requirements of the AI Act. This paper addresses those shortcomings by introducing open-source templates and examples for documenting data, models, and applications to provide sufficient documentation for certifying compliance with the AI Act. These templates track the system status over the entire AI lifecycle, ensuring traceability, reproducibility, and compliance with the AI Act. They also promote discoverability and collaboration, reduce risks, and align with best practices in AI documentation and governance. The templates are evaluated and refined based on user feedback to enable insights into their usability and implementability. We then validate the approach on real-world scenarios, providing examples that further guide their implementation: the data template is followed to document a skin tones dataset created to support fairness evaluations of downstream computer vision models and human-centric applications; the model template is followed to document a neural network for segmenting human silhouettes in photos. The application template is tested on a system deployed for construction site safety using real-time video analytics and sensor data. Our results show that TechOps can serve as a practical tool to enable oversight for regulatory compliance and responsible AI development.