mlsanityData to 7 Oct 2026

The 2024 Foundation Model Transparency Index

arXiv 2407.12929 ↗First posted 17 Jul 2024cs.LG cs.AI cs.CYcited by 18

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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

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  11. 2024RewardBench: Evaluating Reward Models for Language Modelingcited by 480
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22 more
  1. 2024MM1: Methods, Analysis & Insights from Multimodal LLM Pre-trainingcited by 230
  2. 2023Evaluating the Moral Beliefs Encoded in LLMscited by 211
  3. 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 194
  4. 2023A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicitycited by 180
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 6.

18 papers.

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  1. 2024Jamba-1.5: Hybrid Transformer-Mamba Models at Scalecited by 48
  2. 2026The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systemscited by 27
  3. 2025The 2025 Foundation Model Transparency Indexcited by 9
  4. 2025User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policiescited by 8
  5. 2024Toxicity of the Commons: Curating Open-Source Pre-Training Datacited by 7
  6. 2024The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AIcited by 6
  7. 2025The KL3M Data Project: Copyright-Clean Training Resources for Large Language Modelscited by 5
  8. 2025Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluationscited by 4
  9. 2025The Economics of AI Foundation Models: Openness, Competition, and Governancecited by 3
  10. 2025Multi-agent Self-triage System with Medical Flowchartscited by 2
  11. 2026The ATOM Report: Measuring the Open Language Model Ecosystemcited by 2
  12. 2024Generative AI in Medicinecited by 1
6 more
  1. 2025eDIF: A European Deep Inference Fabric for Remote Interpretability of LLMcited by 1
  2. 2025Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmapcited by 1
  3. 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMscited by 1
  4. 2026Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Componentscited by 0
  5. 2026Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routingcited by 0
  6. 2026Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developerscited by 0