mlsanityData to 14 Oct 2026

Foundation Model Transparency Reports

arXiv 2402.16268 ↗First posted 26 Feb 2024cs.LG cs.AI cs.CYcited by 14

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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

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  3. 2018Datasheets for Datasetscited by 1,386
  4. 2018Model Cards for Model Reportingcited by 1,080
  5. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  6. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
  7. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
  8. 2022Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalecited by 262
  9. 2023Stable Bias: Analyzing Societal Representations in Diffusion Modelscited by 226
  10. 2019Principles alone cannot guarantee ethical AIcited by 135
  11. 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 98
  12. 2023Ecosystem Graphs: The Social Footprint of Foundation Modelscited by 32
1 more
  1. 2023Market Concentration Implications of Foundation Modelscited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 4; 2026: 5.

14 papers.

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