Foundation Model Transparency Reports
Not on the map. Why?
Abstract (arXiv)
Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency. To codify how foundation model developers should provide transparency about the development and deployment of their models, we propose Foundation Model Transparency Reports, drawing upon the transparency reporting practices in social media. While external documentation of societal harms prompted social media transparency reports, our objective is to institutionalize transparency reporting for foundation models while the industry is still nascent. To design our reports, we identify 6 design principles given the successes and shortcomings of social media transparency reporting. To further schematize our reports, we draw upon the 100 transparency indicators from the Foundation Model Transparency Index. Given these indicators, we measure the extent to which they overlap with the transparency requirements included in six prominent government policies (e.g., the EU AI Act, the US Executive Order on Safe, Secure, and Trustworthy AI). Well-designed transparency reports could reduce compliance costs, in part due to overlapping regulatory requirements across different jurisdictions. We encourage foundation model developers to regularly publish transparency reports, building upon recommendations from the G7 and the White House.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 13 of its 73 references that are in the map. First posted on arXiv 26 Feb 2024.
Builds on
13 of 73 references.
References · 13
- 2020Measuring Massive Multitask Language Understandingcited by 7,963
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
- 2018Datasheets for Datasetscited by 1,386
- 2018Model Cards for Model Reportingcited by 1,080
- 2021Carbon Emissions and Large Neural Network Trainingcited by 567
- 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
- 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
- 2022Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalecited by 262
- 2023Stable Bias: Analyzing Societal Representations in Diffusion Modelscited by 226
- 2019Principles alone cannot guarantee ethical AIcited by 135
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 98
- 2023Ecosystem Graphs: The Social Footprint of Foundation Modelscited by 32
1 more
- 2023Market Concentration Implications of Foundation Modelscited by 15
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Citing papers per year: 2024: 5; 2025: 4; 2026: 5.
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- 2024The 2024 Foundation Model Transparency Indexcited by 19
- 2024Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?cited by 15
- 2024The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resourcescited by 11
- 2025The 2025 Foundation Model Transparency Indexcited by 11
- 2025The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandatescited by 7
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboardscited by 6
- 2025The Role of Computing Resources in Publishing Foundation Model Researchcited by 2
- 2026The ATOM Report: Measuring the Open Language Model Ecosystemcited by 2
- 2024Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Modelscited by 0
- 2025Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domainscited by 0
- 2026Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarioscited by 0
- 2026Evaluation of ML Resource Utilization Requires Model Life Cycle Assessmentcited by 0
- 2026Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers8 Sep 2026
- 2026When Agents Act Unwatched: The Reduced-Supervision Paradox in Agentic AI28 Aug 2026
- 2026Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment31 May 2026
- 2026Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios8 May 2026
- 2026The ATOM Report: Measuring the Open Language Model Ecosystem8 Apr 2026
- 2025The 2025 Foundation Model Transparency Index11 Dec 2025
- 2025The Role of Computing Resources in Publishing Foundation Model Research15 Oct 2025
- 2025Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains17 Jun 2025
- 2025The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandates16 May 2025
- 2024Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models18 Jul 2024
- 2024The 2024 Foundation Model Transparency Index17 Jul 2024
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards4 Jul 2024
- 2024The 2024 Foundation Model Transparency Indexcited by 19
- 2024Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?cited by 15
- 2024The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resourcescited by 11
- 2025The 2025 Foundation Model Transparency Indexcited by 11
- 2025The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandatescited by 7
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboardscited by 6
- 2025The Role of Computing Resources in Publishing Foundation Model Researchcited by 2
- 2026The ATOM Report: Measuring the Open Language Model Ecosystemcited by 2
- 2024Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Modelscited by 0
- 2025Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domainscited by 0
- 2026Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarioscited by 0
- 2026Evaluation of ML Resource Utilization Requires Model Life Cycle Assessmentcited by 0
12 most cited of 13 All 13 (JSON)
- 2026Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers8 Sep 2026
- 2026Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment31 May 2026
- 2026Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios8 May 2026
- 2026The ATOM Report: Measuring the Open Language Model Ecosystem8 Apr 2026
- 2025The 2025 Foundation Model Transparency Index11 Dec 2025
- 2025The Role of Computing Resources in Publishing Foundation Model Research15 Oct 2025
- 2025Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains17 Jun 2025
- 2025The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandates16 May 2025
- 2024Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models18 Jul 2024
- 2024The 2024 Foundation Model Transparency Index17 Jul 2024
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards4 Jul 2024
- 2024The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources24 Jun 2024
12 newest of 13 All 13 (JSON)