The Foundation Model Transparency Index

A comprehensive assessment of the transparency of foundation model developers

Context. Foundation models like GPT-4 and Llama 2 are used by millions of people. While the societal impact of these models is rising, transparency is on the decline. If this trend continues, foundation models could become just as opaque as social media platforms and other previous technologies, replicating their failure modes.

Design. We introduce the Foundation Model Transparency Index to assess the transparency of foundation model developers. We design the Index around 100 transparency indicators, which codify transparency for foundation models, the resources required to build them, and their use in the AI supply chain.

Execution. For the 2023 Index, we score 10 leading developers against our 100 indicators. This provides a snapshot of transparency across the AI ecosystem. All developers have significant room for improvement that we will aim to track in the future versions of the Index.

Key Findings

The top-scoring model scores only 54 out of 100. No major foundation model developer is close to providing adequate transparency, revealing a fundamental lack of transparency in the AI industry.
The mean score is a just 37%. Yet, 82 of the indicators are satisfied by at least one developer, meaning that developers can significantly improve transparency by adopting best practices from their competitors.
Open foundation model developers lead the way. Two of the three open foundation model developers get the two highest scores. Both allow their model weights to be downloaded. Stability AI, the third open foundation model developer, is a close fourth, behind OpenAI.

Overall Scores for the 10 foundation model providers

Indicators

We define 100 indicators that comprehensively characterize transparency for foundation model developers. We divide our indicators into three broad domains:

Upstream. The upstream indicators specify the ingredients and processes involved in building a foundation model, such as the computational resources, data, and labor used to build foundation models. Full list of upstream indicators.
Model. The model indicators specify the properties and function of the foundation model, such as the model's architecture, capabilities, and risks Full list of model indicators.
Downstream. The downstream indicators specify how the foundation model is distributed and used, such as the the model's impact on users, any updates to the model, and the policies that govern its use. Full list of downstream indicators.

Overall Scores for the 10 foundation model providers broken down by domain — *Scores for the 10 foundation model providers, broken down by domain.*

Scores by subdomain

In addition to the top-level domains (upstream, model, and downstream), we also group indicators together into subdomains. Subdomains provide a more granular and incisive analysis, as shown in the figure below. Each of the subdomains in the figure includes three or more indicators.

Data, labor, and compute are blind spots across developers. Developers are least transparent with respect to the resources required to build foundation models. This stems from low performance on the data, labor, and compute subdomains. All developers' scores sum to just 20%, 17%, and 17% of the total available points for data, labor, and compute.
Developers are more transparent about user data protection and the basic functionality of their model. Developers score well on indicators related to user data protection (67%), basic details about how their foundation models are developed (63%), the capabilities of their models (62%), and their limitations (60%). This reflects some baseline level of transparency across developers regarding how they process user data and the basic functionality of their products.
There is room for improvement even in subdomains where developers are most transparent. No developer provides information about the process by which it provides access to usage data. Only a handful of developers are transparent in demonstrating the limitations of their models or having third parties evaluate models' capabilities. While every developer describes the input and output modality of its model, only three disclose the model components and only two disclose the model size.

Scores for the 10 foundation model providers broken down by 13 subdomains which have three or more indicators. — Scores for the 10 foundation model providers broken down by 13 subdomains, each of which have three or more indicators. Analysis at the level of major subdomains reveals actionable insight into what types of transparency or opacity lead to the above findings.

Open vs. Closed models

One of the most contentious policy debates in AI today is whether AI models should be open or closed. While the release strategies of AI are not binary, for the analysis below, we label models whose weights are broadly downloadable as open. Open models lead the way: We find that two of the three open models (Meta's Llama 2 and Hugging Face's BLOOMZ) score greater than or equal to the best closed model (as shown in the figure on the left), with Stability AI's Stable Diffusion 2 right behind OpenAI's GPT-4. Much of this disparity is driven the lack of transparency of closed developers on upstream issues such as the data, labor, and compute used to build the model (as shown in the figure on the right).

Scores for the 10 foundation model providers broken down by the 13 subdomains which have three or more indicators. — *Open models (Meta's Llama-2, Hugging Face's BLOOMZ, and Stability AI's Stable Diffusion 2) lead the way.*

Disparity is driven by upstream details, such as details about the data, labor, and compute used to develop
the model — The disparity between open and closed models is driven by upstream indicators, such as details about the data, labor, and compute used to develop the model

Methodology

Targets. We selected 10 major foundation model developers based on their influence, heterogeneity, and status as established companies. We assessed these companies on the basis of their most salient and capable foundation model.
Information gathering. We systematically gathered information made publicly available by the developer as of September 15, 2023.
Initial scoring. For each developer, two researchers scored the 100 indicators, assessing whether the developer satisfied the indicator on the basis of public information. We compared scores and resolved disagreements through discussion.
Company response. We shared the initial scores with leaders at each company, encouraging them to contest scores they disagreed with. We addressed their reviews, finalizing scores along with justifications and sources.

About us

The 2023 Foundation Model Transparency Index was created by a group of eight AI researchers from Stanford University's Center for Research on Foundation Models (CRFM) and Institute on Human-Centered Artificial Intelligence (HAI), MIT Media Lab, and Princeton University's Center for Information Technology Policy. The shared interest that brought the group together is improving the transparency of foundation models. See author websites below.

Acknowledgments. We thank Alex Engler, Anna Lee Nabors, Anna-Sophie Harling, Arvind Narayanan, Ashwin Ramaswami, Aspen Hopkins, Aviv Ovadya, Benedict Dellot, Connor Dunlop, Conor Griffin, Dan Ho, Dan Jurafsky, Deb Raji, Dilara Soylu, Divyansh Kaushik, Gerard de Graaf, Iason Gabriel, Irene Solaiman, John Hewitt, Joslyn Barnhart, Judy Shen, Madhu Srikumar, Marietje Schaake, Markus Anderljung, Mehran Sahami, Peter Cihon, Peter Henderson, Rebecca Finlay, Rob Reich, Rohan Taori, Rumman Chowdhury, Russell Wald, Seliem El-Sayed, Seth Lazar, Stella Biderman, Steven Cao, Toby Shevlane, Vanessa Parli, Yann Dubois, Yo Shavit, and Zak Rogoff for discussions on the topics of foundation models, transparency, and/or indexes that informed the Foundation Model Transparency Index. We especially thank Loredana Fattorini for her extensive work on the visuals for this project, as well as Shana Lynch for her work in publicizing this effort.