A Methodology for Creating AI FactSheets
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Abstract (arXiv)
As AI models and services are used in a growing number of highstakes areas, a consensus is forming around the need for a clearer record of how these models and services are developed to increase trust. Several proposals for higher quality and more consistent AI documentation have emerged to address ethical and legal concerns and general social impacts of such systems. However, there is little published work on how to create this documentation. This is the first work to describe a methodology for creating the form of AI documentation we call FactSheets. We have used this methodology to create useful FactSheets for nearly two dozen models. This paper describes this methodology and shares the insights we have gathered. Within each step of the methodology, we describe the issues to consider and the questions to explore with the relevant people in an organization who will be creating and consuming the AI facts in a FactSheet. This methodology will accelerate the broader adoption of transparent AI documentation.
Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jun 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 2; 2024: 3; 2025: 3; 2026: 2.
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- 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
- 2021Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systemscited by 19
- 2022Reward Reports for Reinforcement Learningcited by 18
- 2022Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceabilitycited by 14
- 2023Explain To Me: Salience-Based Explainability for Synthetic Face Detection Modelscited by 8
- 2025Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigationcited by 3
- 2025TechOps: Technical Documentation Templates for the AI Actcited by 3
- 2025Regulating Multifunctionalitycited by 2
- 2021The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learningcited by 1
- 2024Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systemscited by 1
- 2024Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attackscited by 1
- 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Toolscited by 1
- 2026Automatic Model Card Generation Using an LLM25 Aug 2026
- 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Tools17 Feb 2026
- 2025TechOps: Technical Documentation Templates for the AI Act12 Aug 2025
- 2025Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation10 Feb 2025
- 2025Regulating Multifunctionality26 Jan 2025
- 2024Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks15 Oct 2024
- 2024Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems21 May 2024
- 2024Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production14 Feb 2024
- 2023Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models21 Mar 2023
- 2023Fiduciary Responsibility: Facilitating Public Trust in Automated Decision Making6 Jan 2023
- 2022Reward Reports for Reinforcement Learning22 Apr 2022
- 2022Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability13 Apr 2022
3 more
- 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
- 2021Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systemscited by 19
- 2022Reward Reports for Reinforcement Learningcited by 18
- 2022Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceabilitycited by 14
- 2023Explain To Me: Salience-Based Explainability for Synthetic Face Detection Modelscited by 8
- 2025Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigationcited by 3
- 2025TechOps: Technical Documentation Templates for the AI Actcited by 3
- 2025Regulating Multifunctionalitycited by 2
- 2021The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learningcited by 1
- 2024Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systemscited by 1
- 2024Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attackscited by 1
- 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Toolscited by 1
12 most cited of 15 All 15 (JSON)
- 2026Automatic Model Card Generation Using an LLM25 Aug 2026
- 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Tools17 Feb 2026
- 2025TechOps: Technical Documentation Templates for the AI Act12 Aug 2025
- 2025Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation10 Feb 2025
- 2025Regulating Multifunctionality26 Jan 2025
- 2024Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks15 Oct 2024
- 2024Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems21 May 2024
- 2024Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production14 Feb 2024
- 2023Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models21 Mar 2023
- 2023Fiduciary Responsibility: Facilitating Public Trust in Automated Decision Making6 Jan 2023
- 2022Reward Reports for Reinforcement Learning22 Apr 2022
- 2022Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability13 Apr 2022
12 newest of 15 All 15 (JSON)