11affiliationtext: Stanford University22affiliationtext: Massachusetts
Institute of Technology33affiliationtext: Princeton University

Stanford Center for Research on Foundation Models (CRFM)
Stanford Institute for Human-Centered Artificial Intelligence (HAI)

# The Foundation Model Transparency Index

Rishi Bommasani  Kevin Klyman
Shayne Longpre  Sayash Kapoor  Nestor Maslej  Betty Xiong  Daniel Zhang
Percy Liang

###### Abstract.

Foundation models have rapidly permeated society, catalyzing a wave of
generative AI applications spanning enterprise and consumer-facing contexts.
While the societal impact of foundation models is growing, transparency is on
the decline, mirroring the opacity that has plagued past digital technologies
(e.g. social media). Reversing this trend is essential: transparency is a
vital precondition for public accountability, scientific innovation, and
effective governance. To assess the transparency of the foundation model
ecosystem and help improve transparency over time, we introduce the Foundation
Model Transparency Index. The 2023 Foundation Model Transparency Index
specifies 100 fine-grained indicators that comprehensively codify transparency
for foundation models, spanning the upstream resources used to build a
foundation model (e.g. data, labor, compute), details about the model itself
(e.g. size, capabilities, risks), and the downstream use (e.g. distribution
channels, usage policies, affected geographies). We score 10 major foundation
model developers (e.g. OpenAI, Google, Meta) against the 100 indicators to
assess their transparency. To facilitate and standardize assessment, we score
developers in relation to their practices for their flagship foundation model
(e.g. GPT-4 for OpenAI, PaLM 2 for Google, Llama 2 for Meta). We present 10
top-level findings about the foundation model ecosystem: for example, no
developer currently discloses significant information about the downstream
impact of its flagship model, such as the number of users, affected market
sectors, or how users can seek redress for harm. Overall, the Foundation Model
Transparency Index establishes the level of transparency today to drive
progress on foundation model governance via industry standards and regulatory
intervention.

Scores for 10 major foundation model developers across 13 major dimensions of
transparency.

###### Contents

  1. 1 Introduction
    1. 1.1 Findings
    2. 1.2 Recommendations
    3. 1.3 Contributions
  2. 2 Background
    1. 2.1 Foundation models
    2. 2.2 Transparency
    3. 2.3 Indexes
  3. 3 The Foundation Model Transparency Index
  4. 4 Indicators
    1. 4.1 Upstream indicators
    2. 4.2 Model indicators
    3. 4.3 Downstream indicators
  5. 5 Foundation model developers
    1. 5.1 Selecting developers
    2. 5.2 Selecting flagship models
  6. 6 Scoring
  7. 7 Analysis
    1. 7.1 Overarching results
    2. 7.2 Upstream results
    3. 7.3 Model results
    4. 7.4 Downstream results
    5. 7.5 Results for open and closed developers
    6. 7.6 Correlations between companies
  8. 8 Recommendations
    1. 8.1 Recommendations for foundation model developers
    2. 8.2 Recommendations for foundation model deployers
    3. 8.3 Recommendations for policymakers
  9. 9 Impact
    1. 9.1 Theory of change
    2. 9.2 Limitations and risks
  10. 10 Conclusion
  11. A Author contributions
  12. B Indicators
  13. C Search protocol
    1. C.1 General search process
      1. C.1.1 Keyword Definitions
      2. C.1.2 Model-Item Pair Searches
      3. C.1.3 Search Methodology
      4. C.1.4 Justification
      5. C.1.5 Avoid Search Personalization
      6. C.1.6 Determination Criteria
      7. C.1.7 Distribution Channels
    2. C.2 Developer website
    3. C.3 Centralized resources for all models
      1. C.3.1 AI21 Labs (Jurassic-2)
      2. C.3.2 Amazon (Titan Text)
      3. C.3.3 Anthropic (Claude 2)
      4. C.3.4 Cohere (Command)
      5. C.3.5 Google (PaLM 2)
      6. C.3.6 Hugging Face (BLOOMZ)
      7. C.3.7 Inflection (Inflection-1)
      8. C.3.8 Meta (Llama 2)
      9. C.3.9 OpenAI (GPT-4)
      10. C.3.10 StabilityAI (Stable Diffusion 2)
  14. D Calls for transparency

##  1\. Introduction

Foundation models (FMs) like LLaMA and DALL-E 3 are an emerging class of
digital technology that has transformed artificial intelligence (Bommasani et
al., 2021). These resource-intensive models are often built by processing
trillions of bytes of data, with some of the most capable systems, like
OpenAI’s GPT-4, costing hundreds of millions of dollars to
build.111https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-
ai-models-is-already-over/ Foundation models power some of the fastest-growing
consumer technologies in
history,222https://www.reuters.com/technology/chatgpt-sets-record-fastest-
growing-user-base-analyst-note-2023-02-01/ including myriad generative AI
applications,333https://www.mckinsey.com/capabilities/mckinsey-digital/our-
insights/the-economic-potential-of-generative-ai-the-next-productivity-
frontier bringing immense commercial investment and public awareness to AI.
Simultaneously, these models have captured the interest of policymakers around
the world: the United States,444https://www.whitehouse.gov/wp-
content/uploads/2023/07/Ensuring-Safe-Secure-and-Trustworthy-AI.pdf
China,555http://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm
Canada,666https://ised-isde.canada.ca/site/ised/en/voluntary-code-conduct-
responsible-development-and-management-advanced-generative-ai-systems the
European Union,777https://www.europarl.europa.eu/news/en/press-
room/20230609IPR96212/meps-ready-to-negotiate-first-ever-rules-for-safe-and-
transparent-ai the United Kingdom,888https://www.gov.uk/cma-cases/ai-
foundation-models-initial-review India,999https://indiaai.s3.ap-
south-1.amazonaws.com/docs/generative-ai-report.pdf
Japan,101010https://english.kyodonews.net/news/2023/10/3b83adf1e28d-japans-ai-
draft-guidelines-ask-for-measures-to-address-overreliance.html the
G7,111111https://www.politico.eu/wp-
content/uploads/2023/09/07/3e39b82d-464d-403a-b6cb-
dc0e1bdec642-230906_Ministerial-clean-Draft-Hiroshima-Ministers-
Statement68.pdf and a wide range of other governments have already taken
action on foundation models and generative AI. Foundation models are
positioned to be the defining digital technology of the decade ahead.

Transparency is an essential precondition for public accountability,
scientific innovation, and effective governance of digital technologies.
Without adequate transparency, stakeholders cannot understand foundation
models, who they affect, and the impact they have on society. Historically,
digital technologies often follow a familiar pattern: a new technology
provides opportunities and benefits, but companies are not transparent in how
they develop and deploy the technology, and this opacity eventually leads to
harm. In the case of social media, companies have not been transparent about
the ways in which they moderate content and share user data, contributing to
massacres like the Rohingya genocide in Myanmar121212/https://about.fb.com/wp-
content/uploads/2018/11/bsr-facebook-myanmar-hria_final.pdf and gross
violations of privacy like the Cambridge Analytica
scandal.131313https://www.nytimes.com/2018/04/04/us/politics/cambridge-
analytica-scandal-fallout.html Consequently, a chorus of academics, civil
society organizations, firms, and governments have called for foundation model
developers to improve transparency.141414See Appendix D for further
discussion. Groups such as the Partnership on AI, Mozilla, and Freedom House
have noted that increased transparency is a crucial
intervention.151515http://partnershiponai.org/wp-content/uploads/2021/08/PAI-
Responsible-Sourcing-of-Data-Enrichment-Services.pdf UN Secretary-General
António Guterres has proposed that the international community should “make
transparency, fairness and accountability the core of AI governance … [and]
Consider the adoption of a declaration on data rights that enshrines
transparency.”161616https://indonesia.un.org/sites/default/files/2023-07/our-
common-agenda-policy-brief-gobal-digi-compact-en.pdf

Foundation models appear to be on track to replicate the opacity of social
media. Consider OpenAI’s GPT-4, one of the most influential foundation models
today. OpenAI states plainly its intention to be nontransparent in the GPT-4
technical report, which “contains no further details about the architecture
(including model size), hardware, training compute, dataset construction,
training method, or similar” (OpenAI, 2023). Companies often claim that such
information is proprietary or that sharing it would undermine their market
position and pose a danger to society as a whole, but this does not negate the
enormous risks stemming from foundation models these same companies openly
acknowledge, as well as the value of greater transparency.

While the downsides of opacity are clear, transparency in the foundation model
ecosystem today remains minimal. Little to no evidence exists about which
foundation model developers are transparent about which matters, and where
there are blind spots in the industry. How best to improve transparency
remains an open question despite rising concerns.

To determine the status quo and track how it evolves over time, we introduce
the Foundation Model Transparency Index (FMTI).171717See
https://crfm.stanford.edu/fmti. A composite index measures a complex construct
(e.g. transparency) as the basis for scoring/ranking entities (e.g. foundation
model developers) by aggregating many low-level quantifiable indicators of
transparency. Indexes are not common in AI181818We note that the AI Index from
the Stanford Institute for Human-Centered AI (Zhang et al., 2022a; Maslej et
al., 2023) is a related effort, but the AI Index tracks broader trends in AI,
rather than scoring specific entities or aggregating to a single value. but
are a standard methodology in the social sciences: iconic examples include the
United Nations Development Programme’s Human Development Index (UNDP, 2022),
which ranks countries, and Ranking Digital Rights’ Corporate Accountability
Index, which ranks companies (RDR, 2020). We score the transparency of
foundation model developers in an effort to promote responsible business
practices and greater public accountability. We deconstruct the concept of
transparency into 3 high-level domains: the upstream (e.g. the data, labor,
and compute resources used to build a foundation model), model-level (e.g. the
capabilities, risks, and evaluations of the foundation model), and downstream
(e.g. the distribution channels, usage policies, and affected geographies)
practices of the foundation model developer.

##### The 2023 Foundation Model Transparency Index.

For the 2023 index, each domain is further broken down into 32–35 indicators:
these are concrete, specific, and decidable aspects of transparency (e.g. does
the foundation model developer disclose the size of its model?). Ultimately,
the index consists of 100 indicators (see Appendix B) that comprehensively
codify what it means for a foundation model developer to be transparent,
building upon formative works on transparency for AI and other digital
technologies (Gebru et al., 2021; Bender and Friedman, 2018a; Mitchell et al.,
2018; Raji and Buolamwini, 2019; Gray and Suri, 2019a; Crawford, 2021; Vogus
and Llansó, 2021; Keller, 2022).191919See
https://transparency.dsa.ec.europa.eu/ and https://www.tspa.org/curriculum/ts-
fundamentals/transparency-report/

We score 10 major foundation model developers on each of the 100 indicators to
determine how transparent each company is in the development and deployment of
its models. In particular, we score developers based on their practices in
relation to their flagship foundation models: we assess OpenAI (GPT-4),
Anthropic (Claude 2), Google (PaLM 2), Meta (Llama 2), Inflection
(Inflection-1), Amazon (Titan Text), Cohere (Command), AI21 Labs (Jurassic-2),
Hugging Face (BLOOMZ; as host of BigScience),202020Our objective is to assess
Hugging Face as a company that can be tracked over time. BLOOMZ however was
not built unilaterally by Hugging Face, but instead through the BigScience
open collaboration. As a result, we refer to Hugging Face in the prose but
include the BigScience logo in visuals; we provide further discussion in §5.2:
model-selection. and Stability AI (Stable Diffusion 2). In addition, for
downstream indicators we consider the flagship or in-house distribution
channel: OpenAI (OpenAI API), Anthropic (Claude API), Google (PaLM API), Meta
(Microsoft Azure),212121Meta announced Microsoft as the ”preferred partner”
for Llama 2 via Azure: https://about.fb.com/news/2023/07/llama-2/ Inflection
(Pi), Amazon (Bedrock), Cohere (Cohere API), AI21 Labs (AI21 Studio), Hugging
Face (Hugging Face Model Hub), and Stability AI (Stability API). We assess
developers on the basis of publicly-available information to make our findings
reproducible and encourage transparency vis-à-vis the public on the whole. To
ensure our scoring is consistent, we identify information using a rigorous
search protocol (see Appendix C). To ensure our scoring is accurate, we
notified developers and provided them the opportunity to contest any scores
prior to the release of this work (all 10 responded and 8 of the 10 explicitly
contested some scores). We summarize our core findings, recommendations, and
contributions below and make all core materials (e.g. indicators, scores,
justifications, visuals) publicly
available.222222https://www.github.com/stanford-crfm/fmti

###  1.1. Findings

On the basis of conducting the index, we extensively catalogue 35 empirical
findings in §7: results spanning overarching trends, domain-level analyses,
breakdowns for open vs. closed developers, and similarities in developer
practices. We summarize the 10 most critical findings.

##### Significant but obtainable headroom in overall transparency scores
(Figure 7).

Given that the highest overall score is 54 out of 100 and the mean overall
score is 37, all developers have significant room for improvement. In many
cases, such improvement is already feasible: 82 of the indicators are achieved
by some developer, and 71 are achieved by multiple developers.

##### Significant unevenness in overall scores with three major clusters
(Figure 7).

Overall scores vary significantly given a range of 42 between the highest-
scoring developer, Meta, at 54 and the lowest-scoring developer, Amazon, at
12. Relative to the mean overall score of 37, organizations group into three
clusters: four well-above the mean (Meta, Hugging Face, OpenAI, Stability AI),
three around the mean (Google, Anthropic, Cohere), and three well-below the
mean (AI21 Labs, Inflection, Amazon).

##### Upstream resource transparency scores the worst (Figure 8).

Breaking down the trends by domain, scores are consistently the worst for the
upstream domain, particularly the Data, Data Labor, and Compute subdomains.
Several developers (AI21 Labs, Inflection, Amazon) receive 0 points across the
entire set of 32 indicators for the upstream domain.

##### Several upstream matters surrounding data creation are fully opaque
(Figure 10).

Within the upstream domain, no company scores points for indicators about data
creators, the copyright and license status of data, and mitigations related to
copyright. The industry-wide lack of transparency on these issues relates
directly to pressing societal concerns related to copyright and intellectual
property, which are the subject of ongoing litigation.

##### Transparency is highest, but still imperfect, for very basic model
information and downstream distribution (Figure 9).

Breaking down the trends by major dimensions of transparency, the highest-
scoring dimensions are Methods, Model Basics, Capabilities, and Distribution.
However, even when considering indicators for these high-scoring dimensions of
transparency, most companies do not reveal basic information like model size
nor do they explain how or why they made certain release decisions.

##### Developer transparency on Capabilities does not translate to
transparency on Limitations, Risks, and Model Mitigations (Figure 11).

Within the model domain, we consider Capabilities, Limitations, Risks, and
Model Mitigations as four tightly-related subdomains that characterize a
model’s potential societal impact. While many developers score well on
Capabilities by describing, demonstrating, and evaluating capabilities, which
reflect their models’ strengths, the same cannot be said for the other three
subdomains. Instead, transparency is significantly worse: just two developers
demonstrate limitations, none evaluate multiple intentional harms their models
could facilitate, and none provide either externally reproducible or third-
party assessments of mitigation efficacy.

##### There is virtually no transparency about the downstream impact of
foundation models (Figure 12).

Within the downstream domain, no developer provides any transparency into the
affected market sectors, affected individuals, affected geographies, or any
form of usage reporting. Overall, the average score on the Impact subdomain is
the worst in the entire index at 11%; only three developers provide even the
most minimal characterization of the number of downstream applications, and no
developer provides a mechanism for users to seek redress.

##### Open developers are consistently more transparent than closed developers
(Figure 13).

Breaking down trends by how developers release their models, open developers
(i.e. those that release model weights and, potentially, data) show a clear
edge in transparency over closed counterparts (e.g. API providers). Two of the
three open developers (Meta and Hugging Face) score better than all other
developers, while the third (Stability AI) scores one point below the highest-
performing closed developer (OpenAI). Open developers have higher average
scores on 17 of the 23 subdomains.

##### Open developers are much more transparent on upstream resources and
comparably transparent on downstream use when compared to closed developers
(Figure 13).

The average score for open developers on upstream indicators is 53% compared
to a paltry 9% of closed developers. However, while closed developers have
greater control over the downstream use of their foundation models, this does
not translate to greater downstream transparency as the average for open
developers on downstream indicators is 49% compared to 43% from closed
developers.

##### Some companies have highly correlated scores (Figure 14).

Considering pairs of companies, we analyze the extent to which they agree on
the indicators where they do and do not score points. In particular, the three
members of the Frontier Model Forum (Anthropic, Google, OpenAI) exhibit high
indicator-level similarity, as do the two companies that release both model
weights and data (Hugging Face, Stability AI) and the four lowest-scoring
companies (Cohere, AI21 Labs, Inflection, Amazon). This leaves Meta as the
sole outlier in terms of developer-developer indicator-level similarity.

###  1.2. Recommendations

On the basis of our findings, we make specific recommendations aimed at
foundation model developers, foundation model deployers, and policymakers in
§8: recommendations. We highlight our top-most recommendation for each
stakeholder group.

##### Foundation model developers should improve transparency by drawing on
the practices of their competitors.

By assessing developers directly, we clarify for each developer the indicators
where they lack transparency. In itself, this provides a clear diagnostic on
where they stand relative to their competitors and, given our justifications
for why transparency on these matters is valuable, why improving transparency
would be beneficial for society. Given that 82 indicators are all satisfied by
some developer, developers can directly consult the practices of their
competitors to provide a clear example of how they might improve their
transparency. There is a tremendous gap between the 82 already-feasible
indicators and the current top score of 54 and the mean score of 37, meaning
there are many areas of low-hanging fruit where developers can readily improve
transparency today.

##### Foundation model deployers should push for greater transparency from
developers.

Foundation models intermediate a growing supply chain: deployers of foundation
models (e.g. cloud service providers and companies that license developers’
models) as well as other downstream actors are influenced by, and can
influence, the transparency of foundation model developers. In particular,
deployers should push developers for greater transparency when making the
decision, and potentially negotiating the contract, to deploy a developer’s
model. Deployers and other downstream actors wield leverage collectively: it
is their downstream use that generates users and revenue for foundation model
developers, meaning they should use this leverage to acquire the necessary
transparency from foundation model developers.

##### Policymakers should prioritize transparency with sufficient precision.

Given the importance of transparency, policymakers should make transparency a
top priority in legislative proposals and regulatory enforcement related to
foundation models. While transparency is already broadly recognized in most
regulatory frameworks for AI, policymakers should be more precise about what
they mean by transparency and the areas in which they hope to reduce opacity
via transparency requirements or other measures. In particular, policymakers
should understand the status quo for transparency (e.g. via the scores we
provide) and use this evidence to inform interventions in the areas where
transparency is most urgently needed (e.g. on Data Labor and Impact, given
these are lowest-scoring dimensions of transparency across the entire supply
chain).

###  1.3. Contributions

To summarize, our contributions are:

  1. (1)

Taxonomy. We taxonomize the vast conceptual space of transparency in the
context of foundation models, following on widespread calls for transparency
(see Appendix D). In particular, we structure the space hierarchically into 3
domains (i.e. upstream, model, downstream), 23 subdomains (e.g. data, compute,
capabilities, risks, distribution, feedback), and 100 decidable and actionable
indicators.

  2. (2)

Scoring of major foundation model developers. We score 10 major foundation
model developers and their flagship foundation models with a standardized
protocol. These developers vary in their company status (e.g. startups, Big
Tech), release strategy (e.g. open weights, restricted API), modalities (e.g.
text-to-text, text-to-image), and involvement in global policy efforts (e.g.
White House voluntary commitments, Frontier Model Forum). We allow developers
to directly contest scores: all 10 developers engaged in correspondence and 8
contested specific scores.

  3. (3)

Empirical findings. Our extensive evaluation yields 35 findings, which ground
existing discourse and sharpen our understanding of the lack of transparency
in the foundation model ecosystem. In many cases, these findings directly bear
on critical global AI policy efforts (e.g. the EU AI Act) and provide the
basis for clear recommendations on how developers may improve their practices
(e.g. by creating centralized documentation artifacts). Our scores offer ample
opportunities for further analysis.

  4. (4)

Legibility and reproducibility. We provide a public website that presents our
findings and recommendations broadly legible to the general
audience.232323https://crfm.stanford.edu/fmti To facilitate further research,
and reproduce our scoring and analyses, we make all core materials (e.g.
indicators, scores, justifications, visuals) publicly
available.242424https://www.github.com/stanford-crfm/fmti

  5. (5)

Theory of change and future versions. Our objective is to simultaneously
articulate the status quo and increase transparency over time. To this end, we
make very explicit our theory of change: we view our work as compiling the
transparency practices across es across companies as an instrument for driving
change (see §9.1: change) and the limitations/risks of our work (see §9.2:
limitations). Critically, we will conduct additional iterations of the index
to track progress over time to work towards a more transparent foundation
model ecosystem.

##  2\. Background

To begin, we provide a brief primer on the three core concepts underlying this
work: foundation models, transparency, and indexes.

###  2.1. Foundation models

Foundation models are the defining paradigm of modern AI, reflecting a broad
shift in the field from bespoke models for individual tasks to more general
models that can be adapted for a wide range of use cases (Bommasani et al.,
2021). In this sense, foundation models belong to the broader class of
general-purpose technologies that have restructured society such as
electricity, the Internet, and smartphones (Bresnahan and Trajtenberg, 1995;
Brynjolfsson et al., 2021; Bommasani et al., 2021; Eloundou et al., 2023).
Building foundation models requires significant resources: immense volumes of
data are processed using immense amounts of computation to yield the
foundation model. Using foundation models often requires substantially fewer
resources in comparison: models can be adapted, often in lightweight fashion
(e.g. through a simple textual interface), for an increasingly wide range of
use cases. The disparity in resource requirements between development and
deployment has yielded a market where a small set of companies build the most
prominent foundation models that are then adopted by thousands of companies
and millions of consumers (Bommasani et al., 2023b; Vipra and Korinek, 2023;
Widder et al., 2023).

Figure 1. Foundation Model Supply Chain. A conceptual depiction of the
foundation model supply chain, beginning with the primary upstream resources
(i.e. data, compute) and transitioning to the foundation model, subsequent
hosts (or distribution channels), and ending with downstream applications.
Image taken with permission from Jones (2023).

The structure of the foundation model paradigm implicates a broader ecosystem
and supply chain (Bommasani et al., 2023b; Cen et al., 2023; Jones, 2023). We
depict a conceptualized view of this supply chain in Figure 1. The supply
chain begins with the upstream resources that are used to build a foundation
model: data, computational hardware, energy, labor, and code. For each of
these resources, a further supply chain exists: for example, data to build
foundation models is often sourced from the Internet, but this data can only
come to be on the Internet as a result of human data-generating process (e.g.
publishing news article, authoring personal blogs, uploading videos to
YouTube, creating music) along with Internet infrastructure (e.g. networking
protocols). Alongside these upstream resources and supply chains, foundation
models are then used as the foundation for supply chains that derive from the
model. In particular, foundation models are made available for downstream use
through distribution channels (e.g. an API to access the model or a host that
facilitates inference using the model). By way of these distribution channels,
foundation models power downstream applications (e.g. commercial products and
services) across a range of market sectors and geographies. For instance,
OpenAI’s GPT-4 powers applications in education (e.g. Khan Academy’s Khanmigo
tutor), finance (e.g. Stripe’s fraud detection tool), banking (e.g. Morgan
Stanley’s internal chatbot), and government (e.g. Iceland’s language
preservation system).252525See https://openai.com/gpt-4 for a list of several
applications built upon OpenAI’s GPT-4. Overall, a comprehensive account of
the societal impact of foundation models, and their transparency in
particular, requires consideration of the different parts of the foundation
model ecosystem (Bommasani et al., 2021, §1.2).

Foundation models have fueled the recent wave of generative AI technologies:
these models can be used to generate fluent text, useful code, photorealistic
images, and compelling audio. New research efforts built foundation models in
an even broader array of domains: biology (Lin et al., 2023), climate change
(Lacoste et al., 2023),
weather,262626https://www.earthdata.nasa.gov/news/weather-ai-fm-workshop
astronomy (Nguyen et al., 2023), radiology (Chambon et al., 2022), and
robotics (Open X-Embodiment Collaboration et al., 2023). Nevertheless, much of
the present public and commercial interest centers on language models (e.g.
Anthropic’s Claude 2, Meta’s Llama 2) and multimodal models with language
interfaces (e.g. Stability AI’s Stable Diffusion 2, OpenAI’s GPT-4). Alongside
their significant capabilities, researchers have highlighted a large number of
potential risks posed by these foundation models spanning malicious uses like
generating disinformation to unintended harms like generating text that
reinforces societal biases (Bender et al., 2021; Bommasani et al., 2021; Abid
et al., 2021; Weidinger et al., 2022). There have also been recent
demonstrations of many concrete harms from language models.272727Partnership
on AI’s AI Incident database (https://incidentdatabase.ai/) and the AI,
Algorithmic, and Automation Incidents and Controversies database
(https://www.aiaaic.org/aiaaic-repository) collect incidents of harm caused by
AI. For a concrete example, see https://www.404media.co/inside-the-ai-porn-
marketplace-where-everything-and-everyone-is-for-sale/.

###  2.2. Transparency

Transparency is broadly understood as the property of being visible and easily
understood (Aristotle, 350 B.C.E; Kalderon, 2015), and is often a fundamental
prerequisite of social responsibility and accountability (Florini, 2007;
Robinson and Acemoglu, 2012).

Transparency is desirable from a variety of standpoints. For example,
transparently disclosing information makes that information available,
shareable, legible, and verifiable. Transparency when conducting a complex
process can make clear the processes’ scope, stakes, and pitfalls (Lathrop and
Ruma, 2010). Similarly, transparency in decision-making can help those who are
not involved in the decision assess the motivations behind the decision, the
evidence used to justify it, as well as its costs and benefits. Various
philosophers, political theorists, scientists, and journalists have emphasized
the importance of transparency across these and other domains (Johnston, 2006;
Florini, 2007; Benkler, 2013; Schudson, 2015). Civil society, grassroots
organizations, and consumers also regularly call for transparency as a
mechanism for fact finding, accountability, and holding organizations
responsible for harm (Heikkilä, 2023; DiResta et al., 2022).282828See Appendix
D for additional details on calls for transparency. For our purposes, we
consider transparency as it relates to the development and use of digital
technologies, with a specific focus on the transparency of the practices of
foundation model developers as measured by the information they share
regarding their models.292929Note that the term ”transparency” is at times
also used to describe efforts to make AI more explainable or interpretable at
the level of specific AI-based predictions or decisions (Liao and Vaughan,
2023; Zou et al., 2023). Such transparency is not the subject of our work.

##### Why transparent matters for digital technologies.

Transparency in digital technologies is particularly relevant for three
reasons. First, new digital technologies, such as AI, are not well understood
by society, often appearing as a black box (Castelvecchi, 2016). Second,
digital technologies are easily rendered invisible, meaning it is difficult
for nonexperts to understand when processes like algorithmic decision-making
are taking place (Ng et al., 2021). Third, these technologies can have a
profound influence on billions of users across society. And yet these
technologies are built by a small cadre of industry actors who do not
represent society as a whole. Under these conditions, transparency functions
as a prerequisite for public accountability and responsible innovation
(Klyman, 2023). Shared visibility engenders public trust and facilitates
interventions in the public interest (Hardin, 2002). Without sufficient
understanding of industry practices, researchers cannot characterize the
societal impact of digital technologies, let alone propose concrete actions to
improve business practices (Pasquale, 2015). While the effects of transparency
are often difficult to measure as they are diffuse and indirect, transparency
helps to expose malpractice and enables the public to respond to such
malpractice.

##### Limitations of transparency.

Transparency is far from sufficient on its own and it may not always bring
about the desired change (Corbett and Denton, 2023). Salient critiques of
transparency include:

  * •

Transparency does not equate to responsibility. Without broad based grassroots
movements to exert public pressure or concerted government scrutiny,
organizations often do not change bad practices (Boyd, 2016; Ananny and
Crawford, 2018).

  * •

Transparency-washing provides the illusion of progress. Some organizations may
misappropriate transparency as a means for subverting further scrutiny. For
instance, major technology companies that vocally support transparency have
been accused of _transparency-washing_ , whereby ”a focus on transparency acts
as an obfuscation and redirection from more substantive and fundamental
questions about the concentration of power, substantial policies and actions
of technology behemoths” (Zalnieriute, 2021).

  * •

Transparency can be gamified. Digital platforms have been accused of
performative transparency, offering less insightful information in the place
of useful and actionable visibility (Ghosh and Faxon, 2023; Mittelstadt,
2019). As with other metrics, improving transparency can be turned into a
game, the object of which is not necessarily to share valuable
information.303030According to Goodhart’s Law, ”when a measure becomes a
target, it ceases to be a good measure” (Goodhart, 1984).

  * •

Transparency can inhibit privacy and promote surveillance. Transparency is not
an apolitical concept and is often instrumentalized to increase surveillance
and diminish privacy (Han, 2015; Mohamed et al., 2020; Birchall, 2021). For
foundation models, this critique underscores a potential tension between
adequate transparency with respect to the data used to build foundation models
and robust data privacy.

  * •

Transparency may compromise competitive advantage or intellectual property
rights. Protections of competitive advantage plays a central role in providing
companies to the incentives to innovate, thereby yielding competition in the
marketplace that benefits consumers. Consequently, work in economics and
management studies have studied the interplay and potential trade-off between
competitive advantage and transparency (Bloomfield and O’Hara, 1999; Granados
and Gupta, 2013; Liu et al., 2023), especially in the discourse on corporate
social responsibility .

Transparency is not a panacea. In isolation, more information about foundation
models will not necessarily produce a more just or equitable digital world.
But if transparency is implemented through engagement with third-party
experts, independent auditors, and communities who are directly affected by
digital technologies, it can help ensure that foundation models benefit
society.

##### Transparency in practice for prior digital technologies

Digital technologies are marked by a long track record of poor transparency.
While each major new technology has dramatically restructured society, the
powerful corporations that build these technologies have wielded outsized
influence and maintained opacity to advance their commercial interests.
Consider the following examples of digital technologies that suffer from a
lack of transparency as well as associated interventions/studies to reduce
opacity: the fight for net neutrality for internet service providers like
Comcast (Service, 2021), web cookies for online advertising like Google Ads
(Englehardt et al., 2015; Englehardt and Narayanan, 2016; Narayanan and
Reisman, 2017), labor practices for crowd-sourcing platforms like Amazon
Mechnical Turk (Gray and Suri, 2019a; Crawford, 2021), wage schemes for ride
sharing platforms like Uber (Rosenblat and Stark, 2016), and dark patterns for
game companies like Epic Games (Commission, 2023).

Stepping through these examples, efforts like the Princeton Web Transparency
Project (Englehardt et al., 2015; Englehardt and Narayanan, 2016; Narayanan
and Reisman, 2017) have unveiled the ecosystem of online third-party tracking
using cookies, which “led to greater public awareness, the cessation of some
privacy-infringing practices, and the creation of new consumer privacy tools.”
Similarly, Rosenblat and Stark (2016) empirically demonstrated that Uber
drivers were the subject of a severely asymmetric power dynamic given the
control exerted by Uber over their drivers, to the detriment of the ride
sharing market. In the context of crowd-sourcing, Gray and Suri (2019a) and
Crawford (2021) demonstrated exploitation of the “ghost” workers powering AI,
such as on Amazon Mechanical Turk, that was made invisible on these platforms.
More recently, these efforts have prompted the scrutiny of lawmakers as to
improve transparency and, thereby, labor conditions. As a final example, dark
patterns have a pervasive practice for myriad technologies, leading to
mismanaged consumer expectations and overall opacity. To this end, the FTC’s
recent inquiry into Epic Games for dark patterns used to deceive gamers, and
particularly children, amounted to a $245M fine on Epic Games (Commission,
2023).

Building on these prior examples, we consider social media more specifically.
Social media platforms provide a vivid example of transparency challenges in
recent years, and the increasing level of acknowledgement among some
technology companies that a baseline level of transparency is a necessity.
Given the profound impact of social media in mediating how humans form
relationships, communicate with each other, buy goods and services, and access
information, a broad body of work argues for greater transparency (see Keller,
2022). Social media platforms have slowly begun to adopt transparency
reporting practices. For example, Facebook now hosts its own Ad
Library313131https://www.facebook.com/ads/library/, Content
Library323232https://transparency.fb.com/researchtools/meta-content-library,
and a transparency center333333https://transparency.fb.com/ that reports on
content enforcement, widely viewed content, regulatory transparency,
government data requests, and intellectual property, among other pieces of
mostly voluntary transparency. In parallel, transparency requirements have
been enshrined in laws like the EU Digital Services Act (Commission, 2022) and
legislative proposals like the U.S. Platform Accountability and Transparency
Act (Coons et al., 2021).

##### Transparency for AI.

With the rise of AI in the past 10 years, its societal impact has received
much greater attention (Barocas and Selbst, 2016; Abebe et al., 2020;
Hutchinson et al., 2021; Bender et al., 2021). Transparency is often
referenced as a core ethical principle undergirding responsible AI (Fjeld et
al., 2020; Hagendorff, 2020).343434See UNESCO’s Recommendation on the Ethics
of Artificial Intelligence, which was adopted by its 193 member states and
constitutes the first global normative instrument on AI ethics. Our
conceptualization of transparency covers several of UNESCO’s 10 principles,
namely Transparency and Explainability. See
https://www.unesco.org/en/artificial-intelligence/recommendation-ethics Jobin
et al. (2019) find that transparency is the most frequently cited principle in
AI ethics guidelines, appearing in 85% of the assessed 84 guidelines.

Given that the standard machine learning pipeline is divided into several
stages, transparency efforts often target different stages.353535As mentioned
previously, the term ”transparency” is also sometimes used in AI to refer to
explainability/interpretability, referring to understanding how a specific
model makes predictions (Zou et al., 2023). In part, the emphasis on this
topic is due to the inscrutability of the deep neural networks that have
powered AI’s rise. However, we focus on structural forms of transparency,
taking a more macroscopic perspective. Documentation efforts are most common
at the level of data (Gebru et al., 2021; Bender and Friedman, 2018a;
Pushkarna et al., 2022) and models (Mitchell et al., 2018; Crisan et al.,
2022), with evaluations providing further insight into models (Deng et al.,
2009; Ribeiro et al., 2020; Perez et al., 2022; Liang et al., 2022c; Bommasani
et al., 2023c). More recently, several efforts have studied the broader
ecosystem-wide transparency of AI and its supply chains (Bommasani et al.,
2023b; Cen et al., 2023), though transparency on the downstream impacts of AI
is comparatively understudied (Narayanan and Kapoor, 2023). The Foundation
Model Transparency Index advances this view, assessing transparency of
foundation models with a comprehensive ecosystem-level approach that spans the
data and broader upstream resources, the foundation models themselves, and the
downstream use and impact.

###  2.3. Indexes

A (composite) index is a standard methodology (OECD et al., 2008; Greco et
al., 2019) for assessing entities (e.g. companies, countries) in relation to a
specific construct (e.g. transparency, responsibility). Methodologically, the
score on an index for a specific entity is the aggregate of multiple low-level
indicators that can be more directly quantified. Composite indexes as a
methodology has seen broad adoption across the social sciences, including to
directly address major political, economic, and societal concerns such as
public corruption (e.g. Transparency International’s Corruption Perceptions
Index; Transparency International, 2023), environmental welfare (e.g. the
World Economic Forum’s Environmental Sustainability Index; Whitford and Wong,
2009) and living standards (e.g. the United Nations Development Programme’s
Human Development Index; Hopkins, 1991). However, indexes have not played a
major role in mainstream AI discourse.363636We highlight the AI Index from the
Stanford Institute for Human-Centered AI (Maslej et al., 2023; Zhang et al.,
2022a), which tracks global progress of AI across a variety of quantitative
indicators. In contrast to the composite indexes here, the AI Index neither
directly scores specific entities nor does it aggregate individual indicators
into a singular aggregate. We also highlight the Generative AI Accountability
Scorecard from Ranking Digital Rights as a forthcoming effort that targets the
generative AI services downstream of foundation models:
https://rankingdigitalrights.org/mini-report/introducing-rdrs-preliminary-
standards-for-generative-ai/.

Indexes are designed to further several objectives and have certain
characteristic strengths (Commission et al., 2008; Saisana and Tarantola,
2002). Most fundamentally, indexes can transform complex and amorphous
constructs into straightforward and concrete scores. Indexes and the aggregate
quantitative metrics they provide can therefore allow for broad engagement on
certain topics, furthering public understanding as well as providing a strong
basis for various forms of decision-making such as regulatory intervention. In
addition, when indexes are maintained over time, they encourage a long-term
focus and can be vital in fostering improvement over time. In this way, while
operating at a different level of abstraction and involving a different set of
design decisions, indexes are analogous to model benchmarks that are
commonplace in AI (Deng et al., 2009; Wang et al., 2019; Liang et al., 2023)
and appeal to a similar theory of change (Donoho, 2017; Ethayarajh and
Jurafsky, 2020; Raji et al., 2021; Bommasani, 2023). Indexes also have
shortcomings: namely, they can be reductive and overly subjective (Saisana and
Tarantola, 2002; OECD et al., 2008; Greco et al., 2019). To design and score
an index, researchers must make simplifying decisions about which indicators
to include, how to weigh those indicators, and how to grade indicators. Beyond
these methodological issues, indexes are subject to a broader conceptual
critique that they may oversimplify concepts that are intrinsically complex,
discarding valuable nuances.373737The literature and theory on composite
indexes is much too extensive to be easily summarized in this brief primer. We
recommend the Handbook on Constructing Composite Indicators: Methodology and
User Guide (OECD et al., 2008) as a proper introduction to the subject:
https://doi.org/10.1787/9789264043466-en.

Indexes may also be subject to gaming, which we discuss more extensively in
§9.2: limitations.

##  3\. The Foundation Model Transparency Index

The Foundation Model Transparency Index scores foundation model developers for
their comprehensive transparency. We discuss specifics on the developers,
indicators, and scoring in subsequent sections. Strategically, our aim is for
the index to clarify discourse on foundation models and AI that is muddled and
lacks grounding in empirical data. We aim to improve the overall transparency
of the AI ecosystem by encouraging foundation model developers to share more
information about the development and deployment of their models. We also
provide a clear taxonomization of the key issues related to transparency and
demonstrate where greater transparency would be especially valuable.
Therefore, the Foundation Model Transparency Index provides a frame of
reference for assessing whether the ecosystem as a whole—and which developers
in particular—become more or less transparency over time. Simultaneously,
given the limitations of indexes, we are fully transparent about our
methodology, including the core decisions on indicator inclusion, indicator
weighting, and indicator scoring. We also discuss methodological shortcomings
relating to each of these decisions in §9.2: limitations. To guard against
unnecessary simplification, we provide discussion and analysis at several
levels of abstraction in §7: results.

Overall, the Foundation Model Transparency Index captures the key dimensions
of transparency that are relevant to foundation models at present. As the
foundation model ecosystem and AI policy evolves over time, the central
questions regarding the transparency of foundation models will evolve as well.
Consequently, we will conduct future versions of the index that adjust the
indicators to reflect these changes. We more expansively discuss our intended
impact (including our theory of change and associated limitations and risks)
in §9: impact.

##  4\. Indicators

We define 100 indicators that comprehensively characterize transparency for
foundation model developers. To select these indicators, we compiled relevant
concepts raised across past scientific literature as well as concerns animated
by public discourse on foundation models and other digital technologies. In
Appendix B we provide specific references for each indicator, and these
references advocate for increased transparency and information sharing related
to the indicator in question. We derived a concrete set of indicators from
this literature, engaging external researchers to converge on the final list
of 100 (see Figure 2). These indicators cover each dimension of the foundation
model supply chain, from the data, compute, and labor required to build
foundation models to model evaluations and developers’ policies to restrict
their use. We divide our indicators into three broad domains as described in
Figure 1: indicators that are upstream of the model, indicators that relate to
the model itself, and indicators that are downstream of the model.

###  4.1. Upstream indicators

The upstream indicators identify the _ingredients and processes_ involved in
building a foundation model. There are 32 upstream indicators, which we
further taxonomize into the following 6 subdomains:

  * •

Data (10 indicators). Assesses transparency regarding the size and composition
of the data used to build the model; the creators whose content is present in
the data; and any steps to curate or augment the data. These indicators also
address transparency regarding the inclusion of personal, copyrighted, or
licensed data.

  * •

Data Labor (7 indicators). Assesses transparency regarding the use of human
labor in producing the data used to build the model, including the wages,
labor protections, employer, and geographic distribution of workers who
contributed to data annotation and curation. These indicators also address
transparency regarding the third parties that foundation model developers
partnered with to construct their models.

  * •

Data Access (2 indicators). Assesses the scope of data access given to
external parties.

  * •

Compute (7 indicators). Assesses transparency regarding the hardware and
computation used to build the model, as well as the resulting energy use and
environmental impacts.

  * •

Methods (4 indicators). Assesses basic technical specifications for the
model’s training stages and objectives, as well as the software frameworks and
dependencies used.

  * •

Data Mitigations (2 indicators). Assesses transparency regarding steps taken
to mitigate data privacy and copyright concerns.

Figure 2. Indicators. The 100 indicators of the Foundation Model Transparency
Index spanning the 3 domains: upstream, model, and downstream.

.

Figure 3. Upstream Indicators. The 32 upstream indicators that span Data, Data
Labor, Data Access, Compute, Methods, and Data Mitigations.

We depict the upstream indicators in Figure 3. Researchers have widely
advocated for greater transparency in relation to Data and Data Access (Bender
and Friedman, 2018b; Gebru et al., 2018; Hutchinson et al., 2021; Dodge et
al., 2021; Bandy and Vincent, 2021) as a means for contextualizing model
capabilities (Sambasivan et al., 2021; Longpre et al., 2023) and risks related
to privacy, bias, and copyright (Buolamwini and Gebru, 2018; Bender et al.,
2021; Kandpal et al., 2022; Sobel, 2017). Data Labor indicators uplift
concerns related to labor practices, include irresponsible or exploitative use
of human labor (Gray and Suri, 2019a; Crawford, 2021; Hao and Seetharaman,
2023; Kittur et al., 2013; Dzieza, 2023; West, 2019). Compute indicators
relate to concerns around the high computational cost and energy expenditure
associated with building foundation models, which can result in environmental
harm (Lacoste et al., 2019; Strubell et al., 2019; Schwartz et al., 2020;
Patterson et al., 2021; Bender et al., 2021; Henderson et al., 2020; Luccioni
and Hernández-García, 2023; Vipra and West, 2023). Data Mitigations indicators
also relate to the growing legal and sociotechnical concerns over data
privacy, copyright, and licensing (Henderson et al., 2023; Brown et al., 2022;
Lee et al., 2023a; Cooper et al., 2023; Saveri et al., 2023).

###  4.2. Model indicators

The model indicators identify the _properties and function_ of the foundation
model. There are 33 model indicators, which we further taxonomize into the
following 8 subdomains:

  * •

Model Basics (6 indicators). Assesses transparency regarding fundamental
information about the model such as modalities, size, and architecture as well
as the presence of centralized model documentation.

  * •

Model Access (3 indicators). Assesses the scope of model access given to
external entities.

  * •

Capabilities (5 indicators). Assesses transparency regarding the capabilities
of the model, including evaluations.

  * •

Limitations (3 indicators). Assesses transparency regarding the limitations of
the model, including evaluations.

  * •

Risks (7 indicators). Assesses transparency regarding the risks of the model,
including evaluations, with specific focus on both unintentional harm (e.g.
bias) and intentional harm (e.g. fraud).

  * •

Model Mitigations (5 indicators). Assesses transparency regarding model-level
mitigations, including evaluations of their efficacy.

  * •

Trustworthiness (2 indicators). Assesses transparency regarding the
trustworthiness of the model, including evaluations.

  * •

Inference (2 indicators). Assesses transparency regarding standardized
inference with the model.

Figure 4. Model Indicators. The 33 model indicators that span Model Basics,
Model Access, Capabilities, Limitations, Risks, Model Mitigations,
Trustworthiness, and Inference.

We depict the model indicators in Figure 4. Model Basics indicators refer to
fundamental information that is expected by model documentation standards
(Mitchell et al., 2019; Crisan et al., 2022; Bommasani et al., 2023b) and,
historically, have been reliably reported in the release of machine learning
models. Model Access indicators reflect literature tied to the spectrum of
model release and the associated differences in external access (Solaiman et
al., 2019; Sastry, 2021; Shevlane, 2022; Liang et al., 2022a; Solaiman, 2023).
The indicators on Capabilities, Limitations, Risks and Model Mitigations are
motivated by a common understanding that these factors jointly influence the
societal impact of machine learning models and AI systems (Tabassi, 2023a;
Weidinger et al., 2023). For these subdomains, the description and
demonstration indicators gauge whether there is some non-technical
articulation and legibility of these concepts, primed by concerns surrounding
public understanding of foundation models.383838See
https://www.gov.uk/government/publications/public-perceptions-towards-the-use-
of-foundation-models-in-the-public-sector. To make these assessments more
rigorous, the evaluation indicators build on the extensive tradition of
evaluation in AI spanning iconic benchmarks like ImageNet (Deng et al., 2009),
broader benchmarks like SuperGLUE (Wang et al., 2019), and extensive meta-
benchmarks like LM-Harness, BIG-bench, HELM and BEHAVIOR (Gao et al., 2021b;
Srivastava et al., 2022; Liang et al., 2023; Srivastava et al., 2021).
Indicators assessing evaluations also highlight the importance of
reproducibility (Lipton and Steinhardt, 2019; Kapoor et al., 2023; Kapoor and
Narayanan, 2023)393939See the ML Reproducibility challenge:
https://paperswithcode.com/rc2022, CodaLab worksheets for reproducible ML:
https://worksheets.codalab.org/, and Joelle Pineau’s reproducibility
checklist: https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf. and
independent assessment (Sandvig et al., 2014; Raji and Buolamwini, 2019;
Metaxa et al., 2021; Costanza-Chock et al., 2022; Raji et al., 2022b; Raji,
2022; Lam et al., 2022; Weidinger et al., 2023), which enable open science and
external verification of developers’ claims about their models. In the case of
risks, finer distinctions between unintentional harms (e.g. biases, toxicity)
and intentional harms (e.g. disinformation, fraud) build on harm taxonomies
(Bender et al., 2021; Bommasani et al., 2021; Weidinger et al., 2021; Tabassi,
2023b; Weidinger et al., 2023). Indicators on trustworthiness and inference
are especially motivated by the Trustworthy ML
Initiative404040https://www.trustworthyml.org/ and MLPerf (Reddi et al., 2020)
respectively, among other works (Brundage et al., 2020; Cammarota et al.,
2020; Kumar et al., 2020; Liu et al., 2022; Shneiderman, 2020; Patterson et
al., 2021; Narayanan et al., 2023).

###  4.3. Downstream indicators

The downstream indicators identify the _use_ of the foundation model,
including details about its release. There are 35 downstream indicators, which
we further taxonomize into the following 9 subdomains:

  * •

Distribution (7 indicators). Assesses transparency regarding the release
process, the distribution channels for the model, and the products and
services that arise through internal use. Additionally, this subdomain
assesses the presence of model licenses, terms of service, and mechanisms for
detecting model-generated content.

  * •

Usage Policy (5 indicators). Assesses transparency regarding the developer’s
acceptable use policy such as restrictions on specific uses or users, as well
as transparency regarding how it enforces such policies.

  * •

Model Behavior Policy (3 indicators). Assesses transparency regarding the
developer’s policy on acceptable and unacceptable model behavior as well as
transparency regarding enforcement of this policy and expectations in the
event of usage policy violations.

  * •

User Interface (2 indicators). Assesses transparency in the user interface for
the developer’s flagship distribution channel, if the channel includes a user
interface.

  * •

User Data Protection (3 indicators). Assesses transparency regarding the
developer’s policies with respect to user data protection, such as how data is
stored, shared, and accessed.

  * •

Model Updates (3 indicators). Assesses transparency regarding the developer’s
versioning protocol, change log, and deprecation policy.

  * •

Feedback (3 indicators). Assesses transparency regarding mechanisms for
reporting feedback on the model, summaries of feedback received, and related
government inquiries.

  * •

Impact (7 indicators). Assesses transparency regarding the downstream impact
of the model on society, such as affected market sectors, individuals, and
geographies. Additionally, this subdomain assesses transparency regarding
downstream applications, usage statistics, and mechanisms for monitoring usage
as well as providing redress in the event of harm to users.

  * •

Downstream Documentation (2 indicators). Assesses the presence of centralized
documentation for downstream use and documentation for responsible downstream
use.

Figure 5. Downstream Indicators. The 35 downstream indicators that span
Distribution, Usage Policy, Model Behavior Policy, User Interface, User Data
Protection, Model Updates, Feedback, Impact, and Downstream Documentation.

We depict the downstream indicators in Figure 5. Given that foundation models
are the basis for a downstream supply chain (Bommasani et al., 2021), the
distribution indicators are informed by the literature on AI supply chains
(Bommasani et al., 2023b; Vipra and Korinek, 2023; Cen et al., 2023; Cobbe et
al., 2023; Widder and Wong, 2023; Brown, 2023) and release practices (Liang,
2022; Solaiman, 2023; Henderson et al., 2023; Kirchenbauer et al., 2023;
Kuditipudi et al., 2023; Liesenfeld et al., 2023). Usage policy indicators
draw from company publications on responsible model deployment (Cohere, 2022)
as well precedents from social media. Model behavior policy indicators are
rooted in literature that discusses AI behavior and trustworthiness, risks,
mitigation and refusal (Kumar et al., 2022; Weidinger et al., 2021; Brundage
et al., 2020; Cammarota et al., 2020; Kumar et al., 2020; Liu et al., 2022;
Reuter and Schulze, 2023). User interface indicators are derived from research
on safety by design and human-centered user interfaces (Wang et al., 2023b;
Nakao et al., 2022). User data protection indicators are inspired by policy
recommendations on user data minimization, privacy, preservation, protection
and contextual integrity (EU, 2016; Brown et al., 2022; Vipra and Myers West,
2023; Winograd, 2023; Nissenbaum, 2024; King, 2020; Mulligan et al., 2016).
Model updates indicators stem from work focused on adequately updating systems
and version control of AI systems (Sathyavageesran et al., 2022; Hashesh,
2023; Chen et al., 2023b). For feedback, impact and downstream documentation,
the indicators were motivated by the literature on algorithmic auditing
(Liang, 2022; Solaiman, 2023; Raji et al., 2022b) as well as transparency
reporting practices for social media.414141See
https://www.tspa.org/curriculum/ts-fundamentals/transparency-report/,
https://transparencyreport.google.com/ and
https://transparency.fb.com/reports/.

##### Note on assessment of indicators.

We assess each indicator based on the information that developers share
publicly about their flagship foundation models and their practices that apply
to these models. Our standard for awarding points on an indicator is that the
developer must explicitly state the information related to the indicator in
its documentation, or it must explicitly point to the information in its
documentation. This implies that if developers are overly vague or do not link
to a key external document for a particular indicator then they do not receive
a point. In addition, if developers explicitly state in their documentation
that they _do not_ carry out a specific action related to an indicator (e.g.
they do not have a mechanism for users to provide feedback) then we generally
award a point for that indicator. We note that this is exceedingly rare and
that, in general, developers share little information about the actions they
do or do not take in the process of developing and deploying foundation
models.

##### Note on inclusion of deployment.

Our view of transparency is expansive, considering the broader supply chain
beyond just foundation models. As we discuss in §2.2: background-transparency,
existing conceptualizations of transparency in AI often consider upstream
resources (especially data) in addition to machine learning models. But these
works and broader public discourse usually do not foreground the downstream
use and impact of AI, even though this is the most direct way in which AI
affects society. To this end, we include the entire downstream domain to bring
greater attention to this vital topic.

In particular, while we are assessing foundation model developers, we assess
them in relation to distribution channels and other factors that determine
their downstream impact. At present, we recognize that characterizing the
downstream impact of foundation models may be challenging, especially for open
model developers. By releasing a model openly, developers may cede the ability
to easily monitor the model’s downstream use and impact. Open model developers
can be fully transparent by being clear about the ways in which they do or do
not monitor downstream use and impact. In addition, we believe in the
potential for greater coordination between foundation model developers and
distribution channels to increase transparency; for example, distribution
channels could supply information about how the model is used to the
foundation model developer. Partnerships with distribution channels that
promote transparency provide a promising means for all foundation model
developers to share more information about the impact their models have on
society.

##  5\. Foundation model developers

Name | Flagship | Release | Input | Output | Status | Headquarters | WH1 | WH2 | WH3 | FMF
---|---|---|---|---|---|---|---|---|---|---
AI21 Labs | Jurassic-2 | API | Text | Text | Startup | Tel Aviv, Israel | ✗ | ✗ | ✗ | ✗
Amazon | Titan Text | API | Text | Text | Big Tech | Seattle, USA | ✗ | ✓ | ✗ | ✗
Anthropic | Claude 2 | API | Text | Text | Startup | San Francisco, USA | ✓ | ✓ | ✗ | ✓
Cohere | Command | API | Text | Text | Startup | Toronto, Canada | ✓ | ✗ | ✓ | ✗
Google | PaLM 2 | API | Text | Text | Big Tech | Mountain View, USA | ✓ | ✓ | ✗ | ✓
Hugging Face | BLOOMZ | Open weights, open data | Text | Text | Startup | Brooklyn, USA | ✓ | ✗ | ✗ | ✗
Inflection | Inflection-1 | No access (API forthcoming) | Text | Text | Startup | Palo Alto, USA | ✗ | ✓ | ✗ | ✗
Meta | Llama 2 | Open weights | Text | Text | Big Tech | Menlo Park, USA | ✗ | ✓ | ✗ | ✗
OpenAI | GPT-4 | API | Text, Images | Text | Startup | San Francisco, USA | ✓ | ✓ | ✗ | ✓
Stability AI | Stable Diffusion 2 | Open weights, open data | Text | Images | Startup | London, UK | ✓ | ✗ | ✓ | ✗

Table 1. Selected foundation model developers. Information on the 10 selected
foundation model developers: the developer name, their flagship model, the
release strategy for the model (see Figure 6), the input and output modalities
for the model, the developer’s status as either Big Tech or Startup, and the
developer’s headquarters. We note which of the developers were involved in the
White House’s initiative for public evaluation of AI systems announced in May
2023 (WH1), voluntary commitments for the management of risks posed by AI
announced in July 2023 (WH2), and commitments by additional organizations on
the same matters of risks by AI announced in September 2023 (WH3).
Additionally, we note which of the developers are founding members of the
Frontier Model Forum, announced in July 2023.

Transparency initiatives in AI (e.g. datasheets and model cards) often
introduce frameworks that support machine learning developers in achieving
greater transparency in their own work. In contrast, we proactively assess
foundation model developers for their transparency using the 100 indicators we
specify. By conducting the assessment ourselves, we sidestep concerns of
uneven uptake that have arisen with past transparency initiatives (e.g. Gebru
et al., 2018; Mitchell et al., 2018) and provide greater consistency in the
scoring of each indicator across developers. Most importantly, scoring many
developers allows for the comparison of their scores, which provides a rich
context for how to improve transparency in the foundation model ecosystem.

Efforts like Ecosystem Graphs (Bommasani et al., 2023b) and the UK Competition
and Markets Authority (CMA) report on the foundation model
market424242https://www.gov.uk/government/publications/ai-foundation-models-
initial-report track the organizations that develop foundation models. At the
time of writing in September 2023, the CMA report documented 160 foundation
models (based on data drawn from Ecosystem Graphs) built by more than 50
organizations.434343https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1185508/Full_report_.pdf#page=22

However, as the CMA report states, a small number of developers control the
majority of the market at present (Vipra and Korinek, 2023). Due to this
intense level of market concentration, we decided to assess 10 major
foundation model developers.

###  5.1. Selecting developers

We considered a variety of selection criteria in choosing the 10 developers to
assess, arriving at the following three principles:

  1. (1)

Impact. We selected developers that have built the most influential foundation
models.

  2. (2)

Diversity. We selected developers that, when considered collectively,
represent many axes of variation in the foundation model ecosystem. For
example, developers that release models along different points on the release
gradient (e.g. open vs. closed, Solaiman, 2023), build models with different
modalities (e.g. text-to-text vs. text-to-image), and occupy different
positions in the market (e.g. startups vs. Big Tech).

  3. (3)

Companies. We selected developers that are established companies as enduring
targets for longitudinal improvement. This to some extent parallels current
regulatory initiatives that explicitly focus on companies as the target of
policy for foundation models.444444See
https://www.blumenthal.senate.gov/imo/media/doc/09072023bipartisanaiframework.pdf.

On this basis, we chose 10 companies that all are influential foundation model
developers: AI21 Labs, Amazon, Anthropic, Cohere, Google, Hugging Face,
Inflection, Meta, OpenAI, and Stability AI. These 10 provide significant
diversity in terms of release strategy (e.g. Anthropic, Meta, and Hugging Face
all release flagship models with different levels of openness; see Figure 6),
modality (e.g. Cohere, OpenAI, and Stability AI all provide different input-
output modalities), and market position (e.g. Google, Inflection, and OpenAI
occupy different market positions).

Additionally, in parallel to our research, the White House made three
announcements involving companies that develop foundation models: a red-
teaming exercise announced in May
2023,454545https://www.whitehouse.gov/briefing-room/statements-
releases/2023/05/04/fact-sheet-biden-harris-administration-announces-new-
actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-
and-safety/ a set of voluntary commitments announced in July
2023,464646https://www.whitehouse.gov/briefing-room/statements-
releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-
commitments-from-leading-artificial-intelligence-companies-to-manage-the-
risks-posed-by-ai/ and another set of voluntary commitments announced in
September 2023.474747https://www.whitehouse.gov/briefing-room/statements-
releases/2023/09/12/fact-sheet-biden-harris-administration-secures-voluntary-
commitments-from-eight-additional-artificial-intelligence-companies-to-manage-
the-risks-posed-by-ai/ Separately, three of the companies we assess jointly
announced the formation of the Frontier Model Forum in July
2023.484848https://blogs.microsoft.com/on-the-issues/2023/07/26/anthropic-
google-microsoft-openai-launch-frontier-model-forum/ When taken together,
these announcements name 16 companies: Adobe, Amazon, Anthropic, Cohere,
Google, Hugging Face, IBM, Inflection, Meta, Microsoft, NVIDIA, OpenAI,
Palantir, Salesforce, Scale AI, and Stability AI. We note that 9 of the 10
companies we selected are within this set of 16 (all but AI21 Labs).

Figure 6. The gradient of release of foundation models. Foundation models can
be fully closed (e.g. only used internally within the company, without public
release), released gradually as their risks and benefits are better understood
(e.g. via a staged rollout involving initial testers), released via a web or
app interface (e.g. users need to visit a website or join a Discord server to
access the model’s outputs), released via a programmatic API (e.g. users can
query the model and receive outputs programmatically), released via
downloadable model weights (e.g. users can access and adapt the model), or
released with the training data alongside downloadable model weights (i.e.
ostensibly maximal openness). For the ten models we consider, one falls under
the fully closed category at the time of writing (Inflection-1), though
Inflection plans to make it available via an API; six are available via an API
(GPT-4, Claude 2, PaLM 2, Jurassic-2, Command, Titan Text); one is
downloadable (Llama 2), and two are released with their model weights as well
as underlying training data downloadable (Stable Diffusion 2 and BLOOMZ). For
simplicity, we at times binarize these distinctions into models with
downloadable weights (”open”) and models without downloadable weights
(”closed”). Image taken with permission from Solaiman (2023).

##### The gradient of release strategies.

The strategies for releasing foundation models differ widely (see Figure 6).
Some developers release the weights of the model as well as the data used,
which allows independent researchers and developers to use the models on their
own and investigate the data. For example, EleutherAI released the weights of
its Neo-X model (Black et al., 2022) along with The Pile, which Neo-X was
trained on (Gao et al., 2021a). Meta released the weights to its OPT model
(Zhang et al., 2022b), but did not release the associated training data. For
our purposes, we will often refer to any release where model weights are made
broadly available as ”open,” which includes the flagship models of Hugging
Face, Meta, and Stability AI.

In contrast, other developers do not release the weights of their flagship
model, retaining greater control over who has access to the model and the
extent to which it may be used externally (if at all). The majority of the
developers we assess provide a programmatic API to query their flagship model
as a black box. Other developers in the ecosystem do not provide a
programmatic API but do allow for some forms of black box access, as
Midjourney does for its text-to-image models that it makes available via a
Discord server.494949See https://docs.midjourney.com/docs/midjourney-discord.
Still other developers provide no external access to their models as is the
case for Google’s Chinchilla model (Hoffmann et al., 2022a) and Meta’s Make-A-
Video model (Singer et al., 2022). For our purposes, we will often refer to
any release where model weights are not made externally available as ”closed,”
which includes the flagship models of AI21 Labs, Amazon, Anthropic, Cohere,
Google, Inflection, and OpenAI.

The overall approach to release is informed by a developer’s business strategy
and perspective on its model’s utility and risks. In particular, many
organizations may adopt different release approaches for different foundation
models. For example, when releasing GPT-4, OpenAI did not disclose many
details about the modeling architecture and training data, citing competition
and safety as the two main reasons.505050Interview with OpenAI’s chief
scientist and co-founder: https://www.theverge.com/2023/3/15/23640180/openai-
gpt-4-launch-closed-research-ilya-sutskever-interview

On the other hand, when releasing the text-to-speech Whisper model (Radford et
al., 2022), OpenAI disclosed many details and released the model weights
openly. For other developers, the release decision may directly relate to
their purpose for building a foundation model in the first place. For example,
the BigScience collaboration led by Hugging Face that led to the BLOOM model
(Le Scao et al., 2022) was explicitly designed to democratize access to
multilingual large language models with capabilities in traditionally
underrepresented languages. As a result, the initiative released model weights
and data.

###  5.2. Selecting flagship models

Almost all major foundation model developers release multiple foundation
models over time and, even at the time of writing, many have multiple salient
foundation models (often across different modalities). For example, OpenAI has
developed GPT, GPT-2, GPT-3, GPT-4, InstructGPT, WebGPT, Codex, CLIP, DALL-E,
DALL-E 2, DALL-E 3, Jukebox, and Whisper among other models. Given that
developers are not guaranteed to provide uniform transparency for each
foundation model (e.g. OpenAI releases the weights openly for some of these
models but not others), we decide to assess developers in relation to their
flagship foundation model. By flagship foundation model, we mean the
foundation model that is most salient and/or capable from the developer based
on our judgment, which is directly informed by the company’s public
description of the model. We provide basic information about each of the
developers and their flagship model in Table 1.515151For OpenAI, we evaluate
GPT-4, which was released in March 2023, not GPT-4V, a model OpenAI released
in September 2023 after we completed our analysis. With respect to input and
output modality, OpenAI (2023) states that GPT-4 is ”a large multimodal model
capable of processing image and text inputs and producing text outputs.”

##### Note on Hugging Face.

In the case of Hugging Face, we are assessing the company in general as an
enduring target over time. However, for this version of the index, we assess
BLOOMZ (Muennighoff et al., 2022), which was collaboratively developed through
the year-long BigScience initiative that was initiated and led by Hugging Face
from May 2021 to May 2022. As a result, we refer to Hugging Face throughout
the prose, but include the BigScience logo in visuals (which may also be
distributed absent the context we provide in this paper) to highlight this
nuance.

##  6\. Scoring

By selecting the indicators and companies, we abstractly specify the form of
the index. By defining each indicator and designating the flagship foundation
model to be assessed for each developer, we move to a more precise
operationalization. To make the index fully precise, we describe how we
sourced the information that was used to assess each developer on each
indicator, resulting in the final scores.

##### Search protocol.

To source information that we use to score developers, we exclusively use
publicly available information provided by developers themselves. We recognize
that this information may be incomplete (e.g. clients or governments may have
greater access to information from the developer), but given that our focus
includes public accountability, and we are academic researchers, we choose to
consider only publicly available information. Given that public information
may change, we use information available as of September 15, 2023.

For each developer, we initially compile a basic set of resources disclosed by
the developer about their model development practices and their flagship
foundation model. To gather information for a specific indicator, we perform a
structured search to identify all relevant information that is public. The
exact details of how we execute this search are provided in Appendix C.

##### Initial scoring.

Having identified the information basis for scoring an indicator, 2
researchers on the team independently scored the developer on the indicator.
This entails specifying a score (i.e. 0 or 1), source used in arriving at that
score (e.g. one or more webpages), and a textual justification for how the
evidence from sources is weighed against the criteria for the indicator in
determining the score. Given these initial score assignments, the researchers
reviewed their scores to identify any errors.

Binary scoring provided several advantages. First, it simplified the scoring
process by allowing researchers to focus on the sharp distinction between 0
and 1 point for each indicator. Second, a narrow criterion for making a binary
scoring decision for each indicator reduced subjectivity in the initial
scoring. Third, by reducing the level of complexity of each indicator we were
able to reduce overlap between indicators, ensuring that we assess distinct
dimensions of transparency. At the same time, binary scoring limits the level
of complexity of each indicator, potentially leaving out valuable information
that can be captured by more complex scoring schemes (cf. Bommasani et al.,
2023a).525252See §9.2: limitations for further discussion.

In some instances, the researchers responsible for the same (indicator,
developer) pair arrived at different scores, indicating disagreement. Given
the systematic information gathering process, the iterative refinement of
indicator definitions, and the binary scoring scheme, we found that
disagreements were fairly infrequent. Disagreements generally related to
relevant information being erroneously neglected by one researcher or
differences in the fine-grained interpretation of how to score an indicator.
Overall, across all 100 $\times$ 10 (indicator, developer) pairs, the
agreement rate was 85.2% (Cohen’s $\kappa=0.67$, indicating substantial
agreement; Landis and Koch, 1977). To resolve disagreements, the researchers
discussed and jointly came to a resolution. Following the disagreement
resolution, the scores were finalized and sources and justifications were
merged to yield an initial set of 1000 (score, source, justification) triples
for all 1000 (indicator, developer) pairs.

##### Company feedback.

Given that these scores constitute a direct assessment of specific companies,
we engaged these companies to provide them with the opportunity to review,
respond, and potentially rebut or contest the scores we assigned. Concretely,
we contacted leaders at each of the companies with (i) a description of the
Foundation Model Transparency Index, (ii) the 100 indicators and their
definitions, and (iii) their 100 (score, source, justification) triples. We
encouraged each company to review our scores, provide any general feedback
and, especially, to directly contest any scores the company viewed as
incorrect (by referencing public information available as of September 15,
2023). Companies were provided two business weeks to respond with clear
assurance that all correspondence would be strictly private.

Of the 10 companies, all 10 responded. Of these, 8 companies (Amazon,
Anthropic, Cohere, Hugging Face, Inflection, Meta, OpenAI, Stability AI)
provided rebuttals for specific scores, which we extensively reviewed. In most
cases, we did not change scores, though some rebuttals led to improvements in
the scores (an average increase of 1.25 points across the 8 developers that
contested on average 8.75 scores). Rather than improving developers’ scores,
these rebuttals often revealed misunderstandings regarding definitions of
indicators or our justifications for scores, leading to more robust
definitions and justifications. Beyond the scores, several companies scheduled
calls with us or provided broader forms of feedback, which provided insight
regarding how they conceptualize best practices for transparency and
responsible AI. Following company feedback, we again verified all scores,
sources, and justifications that constitute the finalized materials used
throughout this paper and made publicly available.

We also notified the companies prior to the release of this paper, responding
to their feedback. In addition, we encouraged companies to provide a public
written response regarding their perspective on this initiative, their
specific scores, and their broader approach as an organization to transparency
and responsible AI as it relates to foundation models. Moving forward, we hope
these organizations implement more transparent practices and we provide
specific recommendations to that effect in §8.1: recommendations-developers.

##  7\. Analysis

The finalized results of the Foundation Model Transparency Index are the
scores for each of the 100 indicators across all 10 companies. These result
are accessible at https://www.github.com/stanford-crfm/fmti, to facilitate
subsequent analyses. Here, we specifically consider overarching trends in the
results, along with more specific trends based on the structure of the index.
Namely, we analyze along the rows/indicators (e.g. domains), the
columns/companies (e.g. release strategy), as well as data-driven trends (e.g.
correlations).

###  7.1. Overarching results

Figure 7. Overall Scores. The overall Foundation Model Transparency Index
score and ranking across all 100 indicators. Figure 8. Scores by Domain. The
aggregate score of each developer broken down by the three domains: upstream,
model, and downstream.  Figure 9. Scores by Major Dimensions of Transparency.
The fraction of achieved indicators in each of the 13 major dimension of
transparency. Major dimension of transparency are large subdomains within the
23 subdomains.

We begin our analysis by first establishing the broad trends when viewing the
index as a whole. We consider results aggregated at the level of a single
overall score per company (Figure 7) as well as the scores broken down into
the 3 domains (upstream, model, downstream; Figure 8). We supplement our
findings on these overarching trends with a more granular consideration of the
major dimensions of transparency in the index in Figure 9.535353 The major
dimensions of transparency we highlight are 13 large subdomains among the 23
subdomains.

##### All developers have significant room for improvement. But most
transparency indicators are very obtainable, having been implemented by at
least one developer.

Based on Figure 7, the highest-scoring developer scores points for 54 of the
100 indicators, and the average score across all developers is 37. This
establishes a pervasive lack of transparency across major foundation model
developers. With that said, for 82 of the 100 indicators, there exists some
developer that scores points, and of these there are 71 where multiple
developers score points. Consequently, there is clear reason to believe that
across all developers, the necessary change to become more transparent is
feasible. That companies’ competitors are more transparent in certain issue
areas suggests that such transparency, even if not fully costless, is unlikely
to cause serious damage to their business. Companies can emulate the higher
level of transparency their competitors exhibit on certain indicators,
providing a precedent and a starting point for improving transparency in the
foundation model ecosystem.

##### Developers show significant variance in overall transparency scores.

While all developers have significant room for improvement, the current
transparency of developers is strikingly uneven. Namely, the range in overall
scores is 42 between the highest-scoring Meta at 54 and the lowest-scoring
Amazon at 12. Even excluding Amazon’s score as especially low, we still see an
effective range of 30 points between Meta and the next lowest Inflection.
Overall, with respect to the mean of 37, the standard deviation is 14.2, which
is quite substantial. The four top-scoring developers (Meta, Hugging Face,
OpenAI, Stability AI) all cluster well above the mean, the next three are very
close to the mean (Google, Anthropic, Cohere), and the three lowest-scoring
developers (AI21 Labs, Inflection, Amazon) are well below the mean. In many
cases, the lowest-scoring developers have clear opportunities for improvement
through straightforward changes related to some of the least challenging
indicators. Examples include improved documentation (e.g. change logs,
versioning protocols, model cards, centralized documentation for downstream
use), clearer language in corporate policies (e.g. usage policies, model
behavior policies, deprecation policies), and disclosing additional
information that is unlikely to have implications for business competitiveness
or safety (e.g. basic details on methods, dependencies, feedback).

##### The Upstream domain sees the worst transparency scores.

To gain additional insight beyond developers’ basic overall scores, we
consider scores broken down by the 3 top-level domains in Figure 8. On this
basis, we see clear evidence that developers are, on average, least
transparent with respect to the upstream resources required to build their
models, such as data, labor, and compute. Concretely, the mean score on
upstream indicators is 7.2 out of 32 (22.5%), compared to 14.1 out of 33
(42.7%) for model indicators and 15.7 out of 35 (44.9%) for downstream
indicators. To confirm this is not overly biased by outliers, we note that the
medians show the same trend: the median score on upstream indicators is 3.5,
compared to 12.5 for model indicators and 16 for downstream indicators. We
specifically highlight that the four lowest-scoring developers overall (Figure
7) also fare the worst on the upstream domain (Figure 8), with Cohere
receiving 3 points and all of AI21 Labs, Inflection, and Amazon receiving 0
points. In contrast, for both the model and downstream domains, all 10
companies receive at least 6 points.

##### Domain-level discrepancies explain some of the differences between
companies with similar overall scores.

We partition the 10 companies into three groups based on whether their overall
score (Figure 7) is well-above (Meta, Hugging Face, OpenAI, Stability AI),
around (Google, Anthropic, Cohere), or well-below (AI21 Labs, Inflection,
Amazon) the mean. Within these groups, while companies receive somewhat
similar scores, we find that their domain-level scores clarify discrepancies
between them. Among the highest scorers, OpenAI is considerably less
transparent on upstream matters (7) as compared to the other three high-
scoring companies (Meta with 14, Hugging Face with 21, Stability AI with 16).
In particular, OpenAI and Stability AI receive the nearly the same overall
score, with OpenAI making up the deficit to Stability AI on upstream
transparency mostly through better model-level transparency (and,
specifically, many of the indicators on evaluations and risks). For the middle
category of Google, Anthropic, and Cohere, the discrepancies are less stark,
but we do see that Cohere is at 3 in the upstream category compared to Google
with 6 and Anthropic with 5. Given the broadly similar scores for these three
developers across all of the domains, we revisit the extent to which they are
correlated at a finer-grained level in §7.6: correlations. Among the three
lowest-scoring developers, we see that AI21 Labs and Inflection are
differentiated by the model domain, with both scoring a zero on the upstream
domain and similarly on the downstream domain.

##### Data, Data Labor, and Compute are pervasive blind spots across
developers.

While the overall and domain-level results provide a basic lay of the land, we
find that the major dimensions of transparency provide the Goldilocks region
for clear and incisive analysis as shown in Figure 9. In particular, these
dimensions of transparency are subdomains with several indicators (so the
subdomain scores are more reliable) that are tied to broadly-understandable
concepts like labor and capabilities. We hone in on the following major
dimensions of transparency: Data, Data Labor, Compute, Methods, Model Basics,
Model Access, Capabilities, Risks, Model Mitigations, Distribution, Usage
Policy, Model Behavior Policy, Model Updates, User Data Protection, Feedback,
and Impact. Analysis at this level reveals actionable insight into what types
of transparency or opacity lead to many of our top findings. For example, we
find that the poor upstream transparency stems from low performance on the
Data, Data Labor, and Compute subdomains; developers average just 20%, 17%,
and 17% for Data, Data Labor, and Compute respectively. In terms of smaller
subdomains, developers on average score 25% of the available points on Data
Mitigations.

##### Model Basics, Capabilities, Limitations, and User Data Protection are
the most transparent subdomains at present, but still short of the ideal.

Developers score the highest proportion of points on indicators related to the
following subdomains: User Interface (85%), Downstream Documentation (70%),
User Data Protection (67%), Model Basics (63%), and Model Updates (63%). This
reflects some baseline level of transparency across developers with respect to
notifying users they are interacting with AI systems, providing centralized
documentation for downstream use, publishing data protection policies, and
disclosing the modalities associated with their model. Still, there are gaps
in even for these subdomains. No developer provides a protocol for accessing
usage data. Most developers (8 of 10) do not disclose the size of their model.
And only half of the developers provide any form of deprecation policy.

###  7.2. Upstream results

Upstream indicators assess transparency regarding the ingredients that go into
the foundation model including data, labor, compute, methods, and code. These
ingredients are important predictors of the capabilities and risks of the
foundation model they produce, as well as externalities of the model
development process (e.g. impacts on human laborers and the environment). As
we show in Figure 8, the upstream indicators are the most sparsely awarded
(22.5% coverage on average). Here, we analyze at the level of subdomains and
indicators based on Figure 10.

##### The Upstream domain shows the greatest spread.

Building on the fact that developers score worst on the upstream domain–with
several developers scoring exactly or nearly 0 points–we find the range in
scores is the greatest for this domain. Namely, only one developer (Hugging
Face) scores more than half of the indicators (21 of the available 32
indicators; 65.6%), yielding a range of 21 when compared to the lowest-scoring
developers: AI21 Labs, Inflection, and Amazon (0 of the available 32
indicators; 0%). We emphasize this striking disparity given that many of the
fundamental societal issues in connection with foundation models relate to
upstream resources: bias, copyright, and privacy in relation to data, worker
protections and fair compensation in relation to labor, environmental impact
and energy expenditure in relation to compute, reproducibility in relation to
methods, and cybersecurity in relation to code.

##### The Methods subdomain is the most transparent in aggregate, while Data
Labor is the least transparent.

Among the upstream subdomains, only Methods shows some degree of coverage,
with six of the developers giving some description of training stages,
training objectives, and dependencies. On the other end of the spectrum, Data
Labor sees little to no coverage with the exception of BLOOMZ, which involved
volunteers providing data. Developers generally share no information about the
use of human labor in their data pipeline, the employer, wages, and
geographical distribution of these workers, instructions they give to data
annotators, or any labor protections they implement. This industry norm of
being nontransparent with respect to data labor is in tension with the fact
that such information is critical to reinforcement learning with human
feedback (Ziegler et al., 2019; Ouyang et al., 2022; Casper et al., 2023).
That data labor is one of the two least transparent subdomains is consistent
with prior work documenting widespread ethical challenges with data labor
(Gray and Suri, 2019a; Crawford, 2021; Hao and Seetharaman, 2023).

##### The Compute subdomain shows major discrepancies among developers.

Meta and Stability AI document some aspects of compute, energy, and hardware
usage, as well as the carbon footprint of model development, whereas many
developers do not. Given the significant compute expenditure required to build
many foundation models, the practice of documenting energy use and
environmental impact is well-established along with associated tooling to
measure these quantities (Lacoste et al., 2019; Strubell et al., 2019;
Schwartz et al., 2020; Luccioni and Hernández-García, 2023). In spite of this,
most developers do not disclose minimal, or sometimes any, details related to
compute usage, particularly with respect to energy usage, carbon footprint,
and environmental impact.

The broader environmental impact of building foundation models is also
essential to consider; although there has been significant public attention
concerning energy expenditure, other matters such as water usage may be of
similar consequence environmentally (Luccioni and Hernández-García, 2023).
Luccioni et al. (2022) provides an excellent example, documenting the embodied
emissions, dynamic consumption, and idle consumption associated with BLOOM (Le
Scao et al., 2022). Given that BLOOMZ is derived from BLOOM, we note the
potential for documentation transience, where prior documentation is not
updated to reflect substantial changes and, therefore, does not correctly
persist to the new asset. In particular, the additional broader environmental
impact of deriving BLOOMZ from BLOOM is not disclosed.

##### Widespread lack of upstream transparency on data creators, data license,
copyrighted data and associated mitigations, and broader environmental impact.

Of the 32 indicators, no company scores points on six of them. These are the
indicators for data creators, data license status, copyrighted data, copyright
mitigations, compute usage and broader environmental impact. For data
creators, in part we believe this reflects the nascent status of methods for
providing web-scale understanding of who created the data (e.g. text, images)
scraped from the Internet. However, we recognize that Hugging Face in
particular has taken important steps to characterize aspects of who created
the data, along with associated metadata for copyright, license, and personal
information, for the ROOTS corpus used to build BLOOM (though not the
additional data involved in building BLOOMZ). With respect to the copyrighted
data and data license status indicators, we emphasize that information related
to these indicators is at issue in ongoing litigation. In particular,
Stability AI has explicitly argued that training foundation models on
copyrighted data is protected by fair use doctrine in the U.S.545454See
https://www.judiciary.senate.gov/imo/media/doc/2023-07-12_pm_-_testimony_-
_brooks.pdf and https://www.documentcloud.org/documents/23589439-openai-
motion-to-dismiss as well as Lemley and Casey (2020). Closed developers may
also view information related to their data as a key competitive advantage, or
be disincentivized to share this information due to a perception of legal
risk. Additionally, we note that we are surprised no developer directly
discloses the compute usage in FLOPs to sufficient precision, though several
disclose information that could be used to compute an estimate or upper bound.

Figure 10. Upstream Scores by Indicator. The scores for each of the 32
upstream indicators.

Figure 11. Model Scores by Indicator. The scores for each of the 33 model
indicators.

##### No upstream indicators are satisfied by all developers.

At the indicator level, there is no upstream indicator for which every
developer receives points. Of course, this is guaranteed by the presence of
(multiple) developers that score 0 points on the entire upstream domain. Even
putting these 3 developers aside, there is no indicator that is satisfied by
all of the remaining 7. The indicators where the greatest number of developers
score points are data curation (all but Anthropic) and model stages (all but
Cohere), which both suggest that developers are generally willing to describe
the basics of the overall pipeline of model development. With that said, we
take the absence of any upstream indicator where all companies score points,
and the fact that 5 or more developers score no points on 30 of 32 upstream
indicators, as strong evidence that upstream transparency is the domain with
the broadest room for improvement.

###  7.3. Model results

Model indicators assess transparency regarding the function of foundation
models, spanning model access, capabilities, risks, limitations, mitigations,
trustworthiness and inference efficiency, as well as basic information about
the model. The indicators in this domain comprehensively characterize the
foundation model as a standalone artifact: what tasks the model can and cannot
perform, what is the model’s basic structure, who has access to the model, and
more. Here, we analyze developers at the level of subdomains and indicators
based on Figure 11.

##### Model subdomains are some of the highest-scoring across the index.

Overall, the mean score on model indicators is 14.1 out of 33 (42.7%) and the
median developer receives 12.5 points (37.9%). With this in mind, several of
the highest-scoring subdomains belong to the model domain. Developers score
best on Model Basics (63%), Capabilities (62%), Limitations (60%), and Model
Access (57%) within the domain. These scores arise partially because of very
generous indicators within these subdomains (e.g. input modality, output
modality, description of capabilities, description of limitations).

##### Transparency on capabilities does not translate to transparency on
limitations, risks, or mitigations.

Of the 33 model indicators, 20 are in the Capabilities, Limitations, Risks,
and Model Mitigations subdomains. Within these subdomains, Capabilities is
clearly the most transparent subdomain: nearly all developers provide
descriptions (9 of 10) and demonstrations (8 of 10) of multiple model
capabilities, with the majority reporting evaluations (6 of 10), half
reporting reproducible evaluations (5 of 10), and few providing third party
evaluations (3 of 10). In general, we see a decline in the number of
developers who score the point from the most rudimentary (i.e. description) to
the most substantive (i.e. third party evaluations) across these four
subdomains. With respect to Capabilities, while we assume most or all
developers conduct internal evaluations, they may not score points on
evaluations indicators because (i) they do not disclose sufficient details
about internal evaluations for these evaluations to be externally
reproducible, (ii) they do not assess multiple capabilities, or (iii) they do
not report the results of the evaluations, perhaps due to a concern that a
model may underperform competitors’ models.

With this in mind, developers consistently score worse on Limitations, Risks,
and Model Mitigations indicators than on Capabilities. For example, only
Cohere receive points for demonstrating limitations, while 8 developers score
points for demonstrating capabilities. These asymmetries where companies are
more willing to share information about capabilities than limitations, risks,
and mitigations are concerning, as they may lead to an inflated sense of trust
in companies’ foundation models. In fact, these asymmetries are especially
pronounced for Risks (average score of 24%) and Model Mitigations (average
score of 26%), given that these scores are considerably worse than the average
scores for Capabilities (62%) and Limitations (60%).

##### Developers score poorly on Trustworthiness, largely in line with Risks
and Model Mitigations.

With respect to the Trustworthiness subdomain, only OpenAI, Cohere, and AI21
Labs provide information about rigorous evaluations of their flagship model
related to robustness, reliability, hallucinations, calibration, or
explainability. Of those developers, only Cohere and AI21 Labs provide
sufficient detail for their evaluations to be deemed externally reproducible
due to their use of the HELM benchmark (Liang et al., 2023), compared to
OpenAI’s unclear description of their evaluations of model calibration. Given
the previous asymmetry we establish around greater disclosure of capabilities
as compared to limitations, risks, and mitigations, the absence of
trustworthiness evaluations exacerbates these concerns. Put together, the lack
of sufficient public information on limitations, risks, mitigations, and
trustworthiness makes it more likely that consumers will not have well-
calibrated expectations. In turn, this could lead to undesirable overreliance
on foundation models because not enough is done to calibrate consumers on the
appropriate levels of trust (Parasuraman and Manzey, 2010).555555See
https://www.theverge.com/2023/5/30/23741996/openai-chatgpt-false-information-
misinformation-responsibility as an example. With this said, we do acknowledge
that developers may take other routes towards improving trustworthiness
including methods like reinforcement learning from human feedback (Ziegler et
al., 2019; Ouyang et al., 2022, RLHF;) and constitutional AI (Bai et al.,
2022), though transparency is lacking on these approaches (Casper et al.,
2023).

##### Model Access reveals slight differences beyond just release strategy.

In aggregate, companies score 17 of the 30 points (57%) in the Model Access
subdomain across the 3 indicators and 10 companies. On the external model
access protocol indicator, Meta, Hugging Face, OpenAI, and Stability AI are
the only developers to score points. We find this particularly interesting
given Meta, Hugging Face and Stability AI release their models openly in terms
of both model weights and data, whereas OpenAI is considerably more closed,
providing only API access. However, in particular, OpenAI has a clear
researcher access program with a form to request access, criteria it discloses
for granting access, and a period of 4–6 weeks disclosed as the expected
turnaround for a decision. This demonstrates that developers across the
release spectrum (Solaiman, 2023) may achieve transparency on some indicators
while taking substantively different approaches. In practice, we find that
several closed developers have access forms that allow external entities
greater access to the model, but these forms often lack key components of
transparency that clarify the specific steps the developer will take to assess
and grant applications (e.g. in comparison to OpenAI’s process). With that
said, the indicator for full external model access is exclusively achieved by
the three open developers, though every developer other than Inflection
provides black box access access to its model.

##### Model Mitigations are a weak point for most developers.

Developers on average scored just 26% of the total available points on the
five Model Mitigations indicators. Hugging Face, Stability AI, and AI21 Labs
score 0 points, while Cohere, Inflection, and Amazon score only the point on
mitigations description, which is the most lightweight of these indicators. In
general, we highlight an important mismatch between the many risks that are
enumerated and the relatively few mitigations that are described, implemented,
and/or evaluated. Even when mitigations are described, in scoring we find the
mapping between stated risks and stated mitigations is often vague or
nonexistent. Moving forward, we hope developers will directly aim mitigations
at addressing specific risks, with appropriate evaluations to confirm the
efficacy of mitigations in achieving the stated goals.

Figure 12. Downstream Scores by Indicator. The scores for each of the 35
downstream indicators.

##### Most model indicators are scored by some developer, though most
developers score poorly on indicators related to evaluating intentional harms,
mitigations, and inference efficiency.

Of the 33 indicators in the model domain, at least one developer scores a
point on 29 of them. Further, multiple developers score points on 27 model
indicators. The 4 indicators for which no developer scores points are (i)
intentional harm evaluation, (ii) external reproducibility of mitigations
evaluations, (iii) third party mitigations evaluations, and (iv) inference
compute evaluation. The 2 additional indicators for which only one developer
scores points are limitations demonstration (Cohere) and external
reproducibility of internal harm evaluation (OpenAI). While many companies
describe risks (including the risk of intentional harms), they do not share
sufficient information related to evaluations of intentional harm or the
reproducibility of evaluations of mitigations. In the case of inference, we
believe standards are needed akin to MLPerf (Reddi et al., 2020) to rigorously
benchmark the inference of foundation models (Narayanan et al., 2023) given
the key role of efficient inference and low latency in the usability of models
(Lee et al., 2023b). We see that BLOOMZ in particular provides a potential
benchmark for language models by tracking the time spent for a fixed task
(generating 100 tokens given a 7 token prefix) on fixed hardware (a NVIDIA
A100-80GB GPU), though compute is not measured.565656See
https://huggingface.co/blog/habana-gaudi-2-bloom.

###  7.4. Downstream results

Downstream indicators assess transparency regarding the use of foundation
models, spanning subdomains related to distribution, policies constraining the
use and behavior of the model, user interfaces, user data protection, model
updates, feedback, impact, and documentation. Indicators in these subdomains
characterize transparency related to how the foundation model is deployed and
its downstream effects on the ecosystem and society. Our analysis is based on
publicly available information about how the foundation model is distributed,
how it can and cannot be used, how users can give feedback and seek redress,
broader societal impacts, and the how the model affects actors downstream of
the developer in the supply chain. Here, we conduct a fine-grained analysis at
the level of subdomains and indicators based on Figure 12.

##### Downstream scores show less spread across developers.

Total scores on downstream indicators are tightly clustered around the mean of
15.7 out of 35, which corresponds to 44.9% of the 35 downstream indicators.
With the exception of Amazon (6 out of 35; 17.1%), the other nine developers
all score between 14 and 21 points. The highest-scoring on the downstream
domain is OpenAI at 21 points and the lowest-scoring (barring Amazon) are AI21
Labs and Inflection at 14 points. In §7.6: correlations, we clarify the extent
to which these smaller margins in scoring discrepancies in the downstream
domain are due to high agreement in indicator-level scores across companies.

##### Impact is the least transparent subdomain in the entire index.

To clarify the downstream impact of a given foundation model, the Impact
subdomain includes indicators on monitoring mechanisms, affected market
sectors, affected individuals, usage reports, geographic statistics, and
redress mechanisms. Strikingly, the mean score across all developers on this
subdomain is just 11%, with 8 developers scoring points on just 1 of the
possible 7 indicators and the remaining 2 scoring none of the indicators. No
developer scores points on affected market sectors, affected individuals,
usage reports, geographic statistics, or redress mechanism. This means that
there is essentially no information about how many people, sectors, and
regions foundation models are impacting. OpenAI, Google, Cohere, AI21 Labs,
and Inflection are the only developers to disclose a potential monitoring
mechanism for tracking model use. And only open foundation model developers
share limited information about downstream applications, whereas the rest
provide no information.575757We score the downstream applications indicator
quite generously: all of the open developers score points because they
discloses which Hugging Face ”Spaces” are also using the model via Hugging
Face’s platform. However, we emphasize that this is still a poor proxy for the
number of applications dependent on the foundation model.

##### Developers are significantly more transparent about Distribution than
other major dimensions of (downstream) transparency.

Across the four major dimensions of transparency in the downstream domain
(Distribution, Usage Policy, Feedback, Impact), mean scores are on the higher
end only for Distribution at 59%, with the other three all below 50%. Every
developer shares information about distribution channels, or the pathways by
which the model is made available to entities beyond the model developer
organization. Every developer provides terms of service that cover the
distribution of its foundation model.585858As with several downstream
indicators, we assessed the terms of service of the primary distribution
channel. For example, this meant that we assessed Microsoft Azure’s terms of
service for Meta. Most developers share information about their process for
releasing their flagship model (8 of 10) as well as the developer’s products
and services that use the foundation model (6 of 10). Half of developers share
information about the license under which the model is distributed.

##### In spite of broad transparency on the Distribution subdomain, developers
are highly opaque around release decisions.

Within the Distribution subdomain, developers score poorly on the release
decision-making protocol indicator; Hugging Face is the only developer that
shares information about its decision-making protocol for release. Although
there has been an extensive focus on release strategies in the literature on
foundation models (Solaiman et al., 2019; Sastry, 2021; Shevlane, 2022; Liang
et al., 2022b; Liang and Reich, 2022; Solaiman, 2023; Widder et al., 2023;
Seger et al., 2023), developers across the release spectrum share very little
information about how and why they release their flagship models. In
particular, we highlight that many of companies we assess have written about
the broader topic of release, but not in a way that is precise to their
specific decisions for their flagship models.595959We note that following
September 15, 2023, Anthropic released information about its approach to
responsible scaling: https://www.anthropic.com/index/anthropics-responsible-
scaling-policy.

##### Usage Policy and Model Behavior Policy subdomain scores are uneven
across developers.

Scores on the Usage Policy subdomain are uneven, with all developers scoring
points on the indicator for permitted, restricted, and prohibited uses, but
only two (OpenAI and Inflection) scoring points on the usage policy violation
appeals indicator. This reflects the lack of industry standards regarding
precisely how foundation model developers should restrict the use of their
models. We found that different developers provide this information in
different types of documentation, ranging from standalone Acceptable Use
Policies to Content Policies to terms in the model license, and that many
developers share some of this information in several different documents.

While developers did provide some transparency on usage policies related to a
user’s obligations, they did not provide a similar level of transparency on
the restrictions they place on their model’s behavior. Scores on indicators in
the Model Behavior Policy subdomain were relatively weaker, with a mean across
the 3 indicators of 23% compared to 44% for the 5 usage policy indicators.
OpenAI, Anthropic, and Inflection are the only developers who provide
information about permitted, restricted, and prohibited model behaviors, while
only Inflection and Stability AI provide information about how they might
enforce such restrictions. OpenAI and Anthropic are the only developers who
make clear how their models are expected to behave in the event that a user
violates the usage policy. In part, we believe the norms and standards around
model behavior are rather immature, meaning that developers do not provide a
clear conceptualization of if/how they impose a model behavior policy. For
example, the role of modeling decisions (e.g. the use of reinforcement
learning from human feedback or constitutional AI) on behaviors (e.g. model
refusals to specific requests) are not made clear.

##### Identical scores on the User Data Protection subdomain across all
developers.

For the User Data Protection subdomain, scores are uniform across developers,
with every developer scoring points on user data protection policy, as well as
permitted and prohibited uses of user data. However, no developer scores
points on usage data access protocol. This may reflect that few, if any,
companies actually share usage data externally, meaning companies may perceive
that the need to develop protocols for sharing such data is limited. However,
developers’ data protection policies include many provisions that would allow
them to share such usage data, and specific protocols for how and when they do
so are not transparent.

##### Developers lack transparency on the Feedback subdomain.

Developers score relatively poorly on Feedback indicators, scoring only 30% of
the available points. While every developer but Amazon has a public mechanism
for collecting feedback on its model, none provide information such as a
feedback summary or details on government inquiries, such as requests for user
data (which social media companies disclose). This is likely a function of how
nascent the foundation model ecosystem is: companies have only been collecting
feedback for a few years, and it took social media companies several years to
respond to public calls for transparency around the feedback they receive from
users and governments. Moving forward, more robust transparency reporting
practices that provide the public with more information regarding these forms
of feedback will likely be necessary.606060For example, consider the EU’s DSA
Transparency Database, implemented on the basis of the Digital Services Act to
provide transparency on content moderation decisions:
https://transparency.dsa.ec.europa.eu/.

##### Developers are fairly transparent on the Model Updates subdomain.

5 of 10 developers provide clear information about their versioning protocol,
change log, and deprecation policy. Inflection and Amazon, however, score zero
points on these indicators, which may be due in part due to the face that
Inflection-1 and Titan Text are at an earlier stage of release than some other
flagship models. While there is a wide variation in the type, specificity, and
quality of documentation provided related to Model Updates, as with other
indicators, we assess these metrics generously and allocate points on the
basis of transparency alone.

##### Developers score well on the User Interface subdomain, though this may
change due to deployments on mobile phones.

Developers scored highly on User Interface indicators (average score of 85%),
with more than half of developers scoring points on both indicators, which
assess if users are told they are interacting with an AI system and if users
are provided appropriate disclaimers. Developers frequently disclose to users
that they are interacting with a specific foundation model by including the
name of the foundation model somewhere in the user interface, while they give
usage disclaimers upon sign-up for the user interface via a link to the terms
of service or usage policy. Unlike all other indicators, we generally had to
make use of step 7 in the and directly interact with developers’ models via a
user interface to assess these indicators. However, Amazon did not have a
publicly available user interface in advance of September 15, 2023, meaning
that it could not receive these points. We initially assessed transparency of
deployments on mobile devices in some cases, though we ultimately did not
consider these deployments for scoring. With that said, we highlight that the
same standard for transparency of user interfaces does not currently appear to
be met by mobile deployments from OpenAI and Inflection. Overall, we believe
in the importance of providing transparency through user interfaces as it can
help foundation models avoid the formation of the ”dark patterns” we have seen
develop with other digital technologies (Mathur et al., 2019). For example, we
highlight that Anthropic does not make clear that a user is interacting with
an AI system, except for the textual description ”Message Claude.”

###  7.5. Results for open and closed developers

Figure 13. Open vs. Closed by Subdomains. The mean score for the 3 open
developers (Meta, Hugging Face, Stability AI) and the 7 closed developers
(OpenAI, Anthropic, Google, Cohere, AI21 Labs, Inflection, Amazon) across each
of the 23 subdomains. Note: the number of indicators per subdomain varies
widely.

Foundation models are released by different developers using a variety of
release strategies (Liang et al., 2022b; Solaiman, 2023). In particular, we
deliberately chose several developers that are more open (e.g. release the
weights of their model, perhaps along with the data used to build the model)
and others that are more closed (e.g. only provide access via an API). The
topic of release and the (reductive) dichotomy of open vs. closed has emerged
as a primary topic of technical and policy research on foundation models
(Solaiman et al., 2019; Sastry, 2021; Shevlane, 2022; Liang et al., 2022b;
Liang and Reich, 2022; Solaiman, 2023; Widder et al., 2023; Seger et al.,
2023). To clarify how transparency differs between the open developers we
assess (i.e. Meta, Hugging Face, Stability AI) and the closed developers (i.e.
OpenAI, Google, Anthropic, Cohere, AI21 Labs, Inflection, Amazon), we
emphasize the distinction in Figure 13.

##### Open developers score higher in aggregate and on every domain.

We establish a clear trend that the open developers score higher overall, with
all three being among the four highest-scoring developers (see Figure 7). In
particular, every open developer is nearly at least as transparent in terms of
aggregate score as the highest-scoring closed developer (OpenAI): Meta and
Hugging Face are at least 5 points higher, and Stability AI is within a point
of OpenAI. Further, this trend is established more strongly through domain-
level analysis, where open developers score higher on average than closed
developers across all domains (i.e. upstream, model, downstream). The mean
score of open developers on upstream indicators is 53% compared to 9% for
closed developers, 51% for open developers on model indicators compared to 39%
for closed developers, and 49% on downstream indicators compared to 43% for
closed developers. To ensure these trends are robust to outliers, we highlight
that the trends hold even when considering medians instead of means (upstream:
50% to 9%, model: 48% to 45%, downstream: 51% to 43%).

We emphasize that our findings confirm common hypotheses that open developers
will in general be more transparent with respect to the upstream resources
required to build their models (which also aligns with some making the data
they use publicly available), but our findings dispute hypotheses that open
developers will be less transparent on downstream matters due to their weaker
control over downstream use. While we believe that closed developers providing
APIs are better positioned to collect information on the downstream use of
their models, in practice these developers do not disclose this information to
provide greater public transparency.

##### Open developers score higher on most subdomains.

Open developers score higher than closed developers on 15 of the 23
subdomains, which account for 68 of the 100 indicators. The mean score of
closed developers is higher than that of open developers on indicators in the
subdomains of Capabilities, Risks, Model Mitigations, Trustworthiness, Usage
Policy, Model Behavior Policy, and Downstream Documentation. We highlight that
these seven subdomains point to two broader themes: closed developers in some
cases may be higher-resourced or face stronger incentives to proactively
address certain matters around responsible AI (e.g. Risks, Model Mitigations,
Trustworthiness). In addition, closed developers often have a closer coupling
between the foundation model we assessed and downstream services, meaning that
certain user-related aspects of transparency are potentially of higher
priority (namely the Usage Policy). For example, many closed developers
provide products built on top of their flagship foundation model, providing
users of their platforms and clients who license their proprietary foundation
models with an opportunity to push for transparency.

The mean score of open developers is higher than closed developers on every
upstream subdomain, with major score differentials especially for the Data,
Compute, and Methods subdomains. Looking at the difference in average scores
by release strategy, we see large disparities in favor of open models in each
domain, with the largest gaps for Data Access (67% to 0%), Methods (92% to
29%), and Data Mitigations (67% to 7%). We also observe similar large
differentials (40%+) for Model Basics, Model Access, and Model Updates. While
less stark, we highlight the superior transparency on average for the
Distribution subdomain as especially surprising given that closed developers
maintain greater control over distribution by virtue of being closed.

##### Indicator-level analysis further demonstrates the disparity between open
and closed developers.

At the indicator level, the median open developer outscores the median closed
developer on 28 indicators (18 upstream, 7 model, 3 downstream), while the
median closed developer scores higher on just 6 indicators (0 upstream, 2
model, 4 downstream). The median open developer and the median closed
developer both score points on 22 indicators and neither scores points on 44
indicators.

##### The open developers we assessed provide greater transparency than their
closed counterparts.

Overall, each level of analysis points in the same direction: open developers
are reliably more transparent. In particular, we highlight that the release of
assets (e.g. model weights, data, code) may be significantly underweighted in
terms of its broader transparency effects. Our findings dispel the belief that
closed developers are more likely to be transparent about downstream matters
due to their greater control over deployment, while emphasizing that both open
and closed developers continue to be extremely opaque in terms of the
downstream impact of their foundation models. With this in mind, we caution
that our assessment is necessarily based on the practices of some of the
highest-resourced open and closed developers, so these trends should not be
taken as sufficient evidence to claim that all open developers are more
transparent than closed developers. And we believe there is ample opportunity
for closed developers to address these gaps in transparency as we discuss in
§8.1: recommendations-developers.

###  7.6. Correlations between companies

Figure 14. Correlations between Companies. The correlation between the scores
for pairs of companies across all indicators. Correlation is measured using
the simple matching coefficient (i.e. agreement rate), which is the fraction
of all indicators for which both companies receive the same score (i.e. both
receive the point or both do not receive the point).

##### Measuring correlations.

The 100 $\times$ 10 scores introduces data-driven structure. In particular, it
clarifies relationships that arise in practice between different regions of
the index. Here, we consider the correlations, in scores, focusing on company-
to-company similarity for simplicity. For example, if two companies receive
similar aggregate scores, is this because they satisfy all the same indicators
or do they score points on two very different sets of indicators?

In Figure 14, we plot the correlation between every pair of companies. To
measure correlation, we report the simple matching coefficient (SMC) or the
agreement rate. The SMC is the fraction of the 100 indicators for which both
companies receive the same score (i.e. both receive a zero or both receive a
1). As a result, a SMC of 0 indicates there is no indicator such that both
companies receive the same score and a SMC of 1 indicates that for all
indicators both companies receive the same score. For this reason, the
correlation matrix is symmetric and guaranteed to be 1 on the diagonal.

To systematically analyze the results, we consider three patterns in the
correlation matrix: (i) individual cells with very small or very large values
(i.e. highly similar or highly dissimilar company pairs), (ii) individual rows
with consistently small, consistently large, or highly varied values (i.e.
unusual companies), and (iii) structural patterns across the correlation
matrix.

##### Strongly correlated company practices.

In terms of the most correlated company pairs, we identify a few regions of
the correlation matrix. First, we identify the three most correlated pairs:
(Cohere, AI21 Labs; SMC = 0.87), (AI21 Labs, Amazon; SMC = 0.85), and
(Inflection, Amazon; SMC = 0.85). These pairs are all among the four lowest-
scoring companies, though we note the inclusion of Cohere is interesting given
Cohere’s overall score (34) is closer to the average (37) and the middle-
scoring group of companies (i.e. including Google and Anthropic). In addition
to these pairs, if we consider the other highly-correlated pairs (SMC $\geq$
0.8), we identify: (Hugging Face, Stability AI; SMC = 0.80), (OpenAI,
Anthropic; SMC = 0.80), and (Google, Anthropic; SMC = 0.80). In particular, we
observe that the company correlations identify clear structure: Hugging Face
and Stability AI are the only two developers to release both data and models
openly, and the trio of OpenAI, Google, and Anthropic are the three members of
the Frontier Model Forum that we assess.