The 2024 Foundation Model Transparency Index
Not on the map. Why?
Abstract (arXiv)
Foundation models are increasingly consequential yet extremely opaque. To characterize the status quo, the Foundation Model Transparency Index (FMTI) was launched in October 2023 to measure the transparency of leading foundation model developers. FMTI 2023 assessed 10 major foundation model developers (e.g. OpenAI, Google) on 100 transparency indicators (e.g. does the developer disclose the wages it pays for data labor?). At the time, developers publicly disclosed very limited information with the average score being 37 out of 100. To understand how the status quo has changed, we conduct a follow-up study after 6 months: we score 14 developers against the same 100 indicators. While in FMTI 2023 we searched for publicly available information, in FMTI 2024 developers submit reports on the 100 transparency indicators, potentially including information that was not previously public. We find that developers now score 58 out of 100 on average, a 21 point improvement over FMTI 2023. Much of this increase is driven by developers disclosing information during the FMTI 2024 process: on average, developers disclosed information related to 16.6 indicators that was not previously public. We observe regions of sustained (i.e. across 2023 and 2024) and systemic (i.e. across most or all developers) opacity such as on copyright status, data access, data labor, and downstream impact. We publish transparency reports for each developer that consolidate information disclosures: these reports are based on the information disclosed to us via developers. Our findings demonstrate that transparency can be improved in this nascent ecosystem, the Foundation Model Transparency Index likely contributes to these improvements, and policymakers should consider interventions in areas where transparency has not improved.
Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 34 of its 80 references that are in the map. First posted on arXiv 17 Jul 2024.
Builds on
34 of 80 references.
References · 34
- 2023Qwen Technical Reportcited by 3,273
- 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
- 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
- 2021Ethical and social risks of harm from Language Modelscited by 957
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
- 2019Green AIcited by 620
- 2024OLMo: Accelerating the Science of Language Modelscited by 599
- 2021Carbon Emissions and Large Neural Network Trainingcited by 561
- 2023The Falcon Series of Open Language Modelscited by 526
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
- 2024RewardBench: Evaluating Reward Models for Language Modelingcited by 480
- 2024Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Researchcited by 475
22 more
- 2024MM1: Methods, Analysis & Insights from Multimodal LLM Pre-trainingcited by 230
- 2023Evaluating the Moral Beliefs Encoded in LLMscited by 211
- 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 194
- 2023A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicitycited by 180
- 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 163
- 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
- 2019Principles alone cannot guarantee ethical AIcited by 134
- 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 100
- 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
- 2023The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AIcited by 73
- 2024Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Modelscited by 67
- 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
- 2023The Foundation Model Transparency Indexcited by 63
- 2024Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Modelscited by 60
- 2021Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpuscited by 46
- 2024A Safe Harbor for AI Evaluation and Red Teamingcited by 46
- 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42
- 2023Ecosystem Graphs: The Social Footprint of Foundation Modelscited by 31
- 2024Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?cited by 15
- 2024Foundation Model Transparency Reportscited by 14
- 2024Responsible Reporting for Frontier AI Developmentcited by 10
- 2020Trustworthy AI Inference Systems: An Industry Research Viewcited by 4
Built on by
Citing papers per year: 2024: 4; 2025: 8; 2026: 6.
18 papers.
Sort:
By area:
- 2024Jamba-1.5: Hybrid Transformer-Mamba Models at Scalecited by 48
- 2026The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systemscited by 27
- 2025The 2025 Foundation Model Transparency Indexcited by 9
- 2025User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policiescited by 8
- 2024Toxicity of the Commons: Curating Open-Source Pre-Training Datacited by 7
- 2024The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AIcited by 6
- 2025The KL3M Data Project: Copyright-Clean Training Resources for Large Language Modelscited by 5
- 2025Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluationscited by 4
- 2025The Economics of AI Foundation Models: Openness, Competition, and Governancecited by 3
- 2025Multi-agent Self-triage System with Medical Flowchartscited by 2
- 2026The ATOM Report: Measuring the Open Language Model Ecosystemcited by 2
- 2024Generative AI in Medicinecited by 1
6 more
- 2025eDIF: A European Deep Inference Fabric for Remote Interpretability of LLMcited by 1
- 2025Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmapcited by 1
- 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMscited by 1
- 2026Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Componentscited by 0
- 2026Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routingcited by 0
- 2026Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developerscited by 0
- 2026Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers8 Sep 2026
- 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs10 Jun 2026
- 2026Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing30 May 2026
- 2026Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components23 May 2026
- 2026The ATOM Report: Measuring the Open Language Model Ecosystem8 Apr 2026
- 2026The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems19 Feb 2026
- 2025The 2025 Foundation Model Transparency Index11 Dec 2025
- 2025Multi-agent Self-triage System with Medical Flowcharts16 Nov 2025
- 2025Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations6 Nov 2025
- 2025The Economics of AI Foundation Models: Openness, Competition, and Governance17 Oct 2025
- 2025User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies5 Sep 2025
- 2025Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap26 Aug 2025
6 more
- 2025eDIF: A European Deep Inference Fabric for Remote Interpretability of LLM14 Aug 2025
- 2025The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models10 Apr 2025
- 2024Generative AI in Medicine13 Dec 2024
- 2024Toxicity of the Commons: Curating Open-Source Pre-Training Data29 Oct 2024
- 2024The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AI7 Oct 2024
- 2024Jamba-1.5: Hybrid Transformer-Mamba Models at Scale22 Aug 2024
- 2024Jamba-1.5: Hybrid Transformer-Mamba Models at Scalecited by 48
- 2024Jamba-1.5: Hybrid Transformer-Mamba Models at Scale22 Aug 2024
- 2025The 2025 Foundation Model Transparency Indexcited by 9
- 2025User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policiescited by 8
- 2024Toxicity of the Commons: Curating Open-Source Pre-Training Datacited by 7
- 2024The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AIcited by 6
- 2025The KL3M Data Project: Copyright-Clean Training Resources for Large Language Modelscited by 5
- 2025Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluationscited by 4
- 2025The Economics of AI Foundation Models: Openness, Competition, and Governancecited by 3
- 2026The ATOM Report: Measuring the Open Language Model Ecosystemcited by 2
- 2024Generative AI in Medicinecited by 1
- 2025eDIF: A European Deep Inference Fabric for Remote Interpretability of LLMcited by 1
- 2025Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmapcited by 1
- 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMscited by 1
12 most cited of 14 All 14 (JSON)
- 2026Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers8 Sep 2026
- 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs10 Jun 2026
- 2026Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components23 May 2026
- 2026The ATOM Report: Measuring the Open Language Model Ecosystem8 Apr 2026
- 2025The 2025 Foundation Model Transparency Index11 Dec 2025
- 2025Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations6 Nov 2025
- 2025The Economics of AI Foundation Models: Openness, Competition, and Governance17 Oct 2025
- 2025User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies5 Sep 2025
- 2025Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap26 Aug 2025
- 2025eDIF: A European Deep Inference Fabric for Remote Interpretability of LLM14 Aug 2025
- 2025The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models10 Apr 2025
- 2024Generative AI in Medicine13 Dec 2024
12 newest of 14 All 14 (JSON)