The 2023 Foundation Model Transparency Index
Authors: Rishi Bommasani
Kevin Klyman
Shayne Longpre
Sayash Kapoor
Nestor Maslej
Betty Xiong
Daniel Zhang
Percy Liang
Venue: Accepted by TMLR ()

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 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.

Published in Transactions on Machine Learning Research (02/2025)
The 2023 Foundation Model Transparency Index
Rishi Bommasani* Stanford University
mlprishhe@staanfrrdccm edu
Kevin Klyman* Stanford University
IllymanSstan edu
Shayne Longpre
siongpre@mediaa.me mit. edu
Massachusetts Institute of Technology
Sayash Kapoor Princeton University
sayashld@princcelon..dd
Nestor Maslej Stanford University
ammasejj@stanford.ccm edu
Betty Xiong Stanford University
ziongb@stanforee edu
Daniel Zhang Stanford University
(thang1052stannfaan @ato@ssstttfffro edu
Percy Liang Stanford University
pliang@cc.stanford.com edu
Reviewed on OpenReview:
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Abstract
Foundation models have rapidly permeated society, catalyzing a wave of generative Al ap- plications spanning enterprise and consumer-facing contexts. While the socictal impact of foundation models is growing, transparency is 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, scientifie innovation, and ef- foctive governance. To assess the transparency of the foundation model ecosystem and help improve transparency over time, we introduce the Foundation Model Transparency In- dex. The 2023 Foundation Model Transparency Index specifies 100 fine-grained indicators that comprchensively codify transparency for foundation models, spanning the upstream resources used to build foundation model (e.g. data, labor, compute), details about the model itself (e.g. size, capabilities, risks), and the downstream use (e.g. distribution chan- nels, 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 OpenA1, PaLM 2 for Google, Llama for Meta). We present 10 top-level findings about the foundation model ecosystem: for exam- ple, 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.

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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 resourc-innensive models are often built by processing trillions of bytes of data, with some of the most capable systems, like OpenAl's GPT-4, costing hundreds of millions of dollars to build[] Foundation models power some of the fastest-growing consumer technologies in
myriad generative AI applications g bringing immense commercial invest- ment and public awareness to AI. Simultaneously, these models have caplured the interest of policymakers around the world: the United States P China D Canada P the European Union / the United Kingdom [ In- dia Japan I the _ 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 gover- nance 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 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 Myanmar P2 and gross violations of privacy like the Cambridge Analytica scandal 13 Consequently, a chorus of academics, civil sociely organizations, firms, and governments have called for foundation model developers to improve transparency Groups such as the Partnership on AI, Mozilla, and Freodom House have noted that incrcased transparency is a crucial inter- vention 15 UN Secretary- Genaral António Guterres has proposed that the international community should "make transparency, fairness and accountability the core of AT governance ... [and] Consider the adoption of a declaration on data rights that enshrines transparency. To
Foundation models appear to be on track to replicate the opacity of social media. Consider OpenAl's GPT-4, one of the most influential foundation models today. OpenAl states plainly its intention to be nontrans- parent 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" (OpenAl 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
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matters, and where there are blind spots in the industry. How best to improve transparency remains open question despite rising concerns.
To determine the status quo and track how it evolves over time, we introduce the Foundation Model Trans- parency Index (FMTI) 77 A composite inder measures a complex construct (e.g. transparency) as the basis for traiiinggraniigggggg. entities (e.g. foundation model developers) by aggregating many low-level quantifiable indicators of transparency. Indexes are not common in A p 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 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 policics, 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 comprchensively codify what it means for foundation model developer to be transparent, building upon formative works on transparency for AT and other digital technologies (Gebru
let al. 2021 Bender & Friedman 2018b Mitchell et al. 2018 Raji & Buolammini] 2019 Gray & Suri 2019a
Crawford 2021: Vogus & Llansó 2021
Keller 2022 19
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 their practices in relation to their flagship foundation models:
assess OpenAl (GPT-4), Authropic (Claude 2), Google (PaLM 2), Meta (Llama 2), Inflection (nflection-1). Amazon (Titan Text), Cohere Commandd, AI21 Labs (Jurassic-2), Hugging Face (BLOOMZ; as host of BigSeience)
20 and Stability AI
(Stable Diffusion 2). In addition, for downstream indicators we cousider the flagship or in- house distribution channel: Open (OpenAT APT), Anthropic (Claude APT), Google (PaL.M APT), Meta (Microsoft. Azure) Inflection (Pi), Amazon (Bedrock), Cohere (Cohere API), A121 Labs (A121 Studio), Hugging Face (Hugging Face Model Hub), and Stability AI (Stability API). We assess developers 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 rigorous search protocol (see Appendix 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
1.1 Findings
On the basis of conducting the index, we extensively catalogue 35 empirical findings in 87 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.
See httpps://rftn atanford, stanford.edu/fatt edu/fati
We note that the Al Index from the Stanford Institute for is a related effort, but the Al Index tracks broader trends in AI, rather than scoring specific entities or aggregating to a single value.
Al (Zhagg et al. 2022a Maslej et al, 2023
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20rrr objective is to assess Hugging Face company that can be tracked over time. BLOOMZ however was not built
unilaterally by Hugging Pace, but instead through the BigScience open collaboration. As result, refer Hugging Face in
the prose but include the BigScience logo in visuals; we provide further discussion in 55. MODEL-SELECTION
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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 dovelopers.
Significant but obtainable headroom in overall transparency scores (Figure 7
Overall scores vary significantly given a range of 42 between the highest-scoring developer, Meta, 54 and the lowest-scoring developer, Amazon, at 12. Relative to the mean overall score 37, organizations group into three clusters: four well-above the mean (Meta, Hugging Face, OpenAI, Stability AT), three around the mean (Google, Anthropie, Cohere), and three well-below the mean (A121 Labs, Inflection, Amazon).
Significant unevenness in overall scores with three major clusters (Figure 7)
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 (A121 Labs, Inflection, Amazon) receive points across the entire set 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 down- stream 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 why they made certain release decisions.
Developer transparency 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 worsc: just two developers demonstrate limitations, none evaluate multiple intentional harms their models could facilitate, and none provide either externally reproducible third-party assessments of mitigation efficacy.
There is virtually no transparency about the downstream impact of foundation models (Fig- ure 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 Break- ing down trends by how developers release their models, open developers (i.c. those that release model weights and, potentially, data) show 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 ggghest-performng 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 transpar- ent on downstream use when compared closed developers (Figure 13). The average score for open developers upstream indicators is 53% compared to paltry 9% of closed developers. However, while
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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.
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, do the two companies that release both model weights and data (Hugging Face, Stability AT) and the four lowest-scoring companies (Cohere, AT21 Labs, Inflection, Amazon). This leaves Meta as the sole outlier in terms of developer-developer indicator-level similarity.
Some companies have highly correlated scores (Figure 14).

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1.2 Recommendations
On the basis of our findings, make specilic recommendations aimed at loundation model developers, foundation model deployers, and policymakers in [8] RECOMMENDATIONS We highlight our top-most rec- ommendation for cach stakcholder 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 clear diagnostic 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 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 growing supply chain: deployers of foundation models (e.g. cloud service providers and companies that license developers' models) well other downstream actors are inftuenced 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 's model. Deployers and other downstream actors wield leverage collectively: is their downstream use that generates users and revenue for foundation model developers, meaning they should use this leverage 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 regula- tory 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 roquirements or other mcasures. In particular, policymakers should understand the status quo for transparency (e.g. via the scores we provide) and use this evidence to inform interventions the areas where transparency is most urgently needed (e.g. on Data Labor and Impact, given these lowest-scoring dimensions of transparency across the entire supply chain).

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1.3 Contributions
To summarize, our contributions are:
Taxonomy. We taxonomize the vast conceptual space of transparency in the context of foundation models, following widespread calls for transparency (see Appendix D In particular, we struc- ture 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. Scoring of major foundation model developers. We 10 major foundation model devel- opers and their flagship foundation models with standardized protocol. These developers vary in their company status (c.g. startups, Big Toch), 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. 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 Al policy efforts (e-g- the EU AT 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. Legibility and reproducibility. We provide a public website that presents our findings and recommendations broadly legible to the general audience 23 To facilitate further research, and reproduce our scoring and analyses, we make all core materials (c.g. indicators, scores, justifications, visuals) publicly available 24
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 change: we view our work as compiling the transparency practices across es across companies as an instrument for driving change (see 99.1 CHANGE and the limitations/rias /riooss of our work (see 99.2 LIMITATIONS) Critically, will conduct. additional iterations of the index to track progress over time work towards more transparent foundation model ecosystem.
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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. (202I). In this sense, foundation models belong to the broader class of general-purpose lechnologies that have restructured society such as electricity, the Internet, and smartphones (Bresnahan) & 'Tratteneerg 1995 Brynjolisson 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 & Korinek 2023 Widder et al. 2023).
The structure of the foundation model paradigm implicates broader ecosystem and supply chain Bommasani (023b); Cen et al. (2023); Jones (2023). We depict conceptualized view of this supply chain in Figure 1 The supply chain begins with the upstream resources that are used to build 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 (c.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, OpenAl's GPT-4 powers applications in education (e.g. Khan Academy's '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) Overall, 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).
et al.
Foundation models have fueled the recent wave of generative AT technologies: these models can be used to generate fluent text, useful code, photorcalistic images, and compelling audio. New research efforts built loundation models in an even broader array of domains: biology (Lin el al. 2023), climate change (Lacoste) et al. 2023), weather astronomy Ngguyen al. 2023). radiology (Chambon et al.) 2022). and robotics (pen X-Embodiment Collaboration et al.) 2023). Neverthcless, much of the present public and commercial interest centers on language models (e.g. Anthropic's Claude 2, Meta's Llama 2) and multinodal models with language interfaces (e.g. Stability AT's Stable Diffusion 2, OpenAT's GPT-4). Alongside their signifi- cant 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
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WHAT: Gathers data at scale
DATA LAYER
COMPUTE LAYER
WHAT: Provides processing power for training
UPSTREAM
WHO: Company collecting/ processing data
WHO: Company providing computational resources
WHAT: Designs foundation model and trains foundation model using data and
FOUNDATION MODEL DEVELOPER LAYER
Foundation model company
HOST LAYER
WHAT: Hosts and makes models available to downstream users via download API
house foundation model company cloud service provider
WHO:
WHAT: Builds and releases application incorporating the foundation model
APPLICATION LAYER
fine-tuned version
WHO: Application developer (may be same foundation model company or third party)
DOWNSTREAM
APPLICATION USER
WHAT: Uses application
WHO: End usere (public. organisations, companies, etc.)
Figure 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 founda- tion model, subsequent hosts (or distribution channels), and ending with downstream applications. Image taken with permission from Jones (2023).

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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 & Acemoglu 2012).
Transparency is desirable from a variety of standpoints. For example, transparently disclosing information makes that information available, sharcable, legible, and verifiable. Transparency when conducting a com- plex process can make clear the processes' scope, stakes, and pitfalls (Lathrop & 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 thcorists, scientists, and journalists have emphasized the importance of transparency across these
and other domains (Johnston 2006 Florini 2007 Benkler 2013 Schudson 2015 Civil sociely, grassroots organizations, and consumers also regularly call for transparency as a mechanism for fact finding, account- ability, and holding organizations responsible for harm (Heikkilä) 2023 DiResta et al. 2022 For our purposes, we consider transparency as it relates to the development and use of digital technologies, with specific focus the transparency of the practices of foundation model developers measured by the information they share regarding their models 29
Why transparent matters for digital technologies. Transpareeny in digital technologies is particu- larly relevant for three reasons. First, new digital technologies, such as AI, are not well understood by socicty, often appearing as a black box (Castelvecchi] 2016). Second, digital Lechnologies are easily rendered invisible, meaning it is difficult for nonexperts to understand when processes like algorithmic decision decision-making are tak- ing place (Ng et al. 2021). Third, these technologies can have 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 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 mcasure 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 & Denton 2023 Salient critiques of transparency include:
Transparency does not equate responsibility. Without broad based grassroots movements to exert public pressure concerted government scrutiny, organizations often do not change bad practices [Boyd 2016
Ananny & Crawlord 2018
Transparency-washhing provides the illusion of progress. Some organizations may misappropriate transparency as means for subverting further scrutiny. For instance, major technology companies that vocally support transparency have been accused of transparency washinnn whereby "a focus transparency acts as an obfuscation and redirection from more substantive and fundamental ques- tions 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 & 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 30
28See Appendix D for additional details calls for transparency. 29oo that the term "transparency" is times also used to describe efforts to make Al more explainable interpretable at
the level of specific Al-based predictions decisions Lino & Vaughan 2023 Zou al. 2023 Such transparency not the
subject of our work. 30 According to Goodhart's Law, "when a measure becomes target, it ceases be good measure'
Goodhart 1984
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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 com-
petitive advantage and transparency (Bloomfield & O'Hara 1999 Granados & 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 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 lack of transparency well associated interventions/ snrevenniionsssuddiss 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 & Narayanan 2016 Narayanan & Reisman 2017).
labor practices for crowd-sourcing platforms like Amazon Mechnical Turk (Gray & Suri, 2019a Crawford 2021), wage schemes for ride sharing platforms like Uber (Rosenblat & Stark] 2016), and dark patterns for game companies like Epic Games (Commission 2023).
Stepping through these examples, efforts like the Princcton Web Transparency Project (Englchardt et al., 2015 Englehardt & Narayanan 2016 Narayanan & Reisman 2017) have unveiled the ecosystem of online third- party tracking using cookies, which "led to greater public awareness, the cossation of some privacy- infringing practices, and the creation of new consumer privacy tools." Similarly, [Rosenblat & 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 & 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 to improve transparency and, thereby, labor conditions. As a final example, dark patterns have pervasive practice for myriad technologies, lcading 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 vivid example of transparency challenges in recent years, and the increasing level of acknewledgement 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, 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 Library 3 Content Library 32 and a transparency center
33 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
3 htt.://www........../...aa/aatta
35 sttp:://traanaprenccy fb. com
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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 & Selbst 2016 Abebe et al., 2020 Hutchinson et al. 2021 Bender et al.] 2021)
Transparency is often referenced
core ethical principle undergirding responsible AI (Fjeld et al. 2020:
Hagendorff 2020 3T Jobin et al. (2019) find that transparency is the most frequently cited principle in AT
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 35
Documentation efforts are most, common at the level of data (Gebru et al. 2021:
Bender & Friedmaa, 2018b;
Pushkarna al. 2022) and models (Mitchell 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
ccosystem-wide transparency of AI and its supply chains (Bommasani et al. 2023b Cen ct al. (202)). though
transparency on the downstream impacts of Al is comparatively understudied (Narayanan & 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 al. 2019) for assessing entities (c.g. companics, countries) in relation to specific construct (e.g- transparency, responsibility). Method- ologically, the score an index for 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 corrup- tion (c.g. Transparency International's Corruption Perceptions Index; Transparency International 2023) environmental welfare (e-g. the World Economic Forum's 's Environmental Sustainability Index; Whitford & Wong 2009) and living standards (e.g. the United Nations Development Programme's 's Human Development
Index:
Hopkins 1991.. b. However, indexes have not played major role in mainstream AI discourse 36
Indexes are designed to further several objectives and have certain characteristic strengths (Commission et al. 2008 Saisana & Tarantola 2002]. Most fundamentally, indexes can transform complex and amor- phous constructs into forwid 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 (Kogen 2022). In this way, while operating at a different level of abstrac- tion and involving 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 appcal to similar theory
of change DDnoho 2017 Ethayarajh & Jurafsky 2020 Raji et al. 2021 Bommasani] 2023). Indexes also
have shortcomings: namely, they can be reductive and overly subjective (Saisana & Tarantola 2002 OECD
See 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 AT ethics. Our concepinalization of transparency covers several ol UNESCO's 10 principles, namely Transparency and Explainability. See battps://wwat unesco. wtt.//////rrriiiaaaateeeaaa
nce As mentioned previously, the term "transparency" is also sometimes used in Al to reter to explamability interreetabbiiityy referring to understanding how 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 Al's rise. However, we focus on structural forms of transparency, taking more macroscopic perspective.
We highlight the Al Index from the Stanford Institute for Cumman-Centered Al (Masle) et al. 20023 Zhang al. [2022a) which tracks global progress of Al across variety of quantitative indicators. In contrast the composite indexes here, the AT Index neither directly scores specific entities nor does it aggregate individual indicators into singular aggregate. We also highlight the Generative Al Accountability Scorecard from Ranking Digital Rights forthcoming effort that targets the
generative services downstream of foundation models: https::/eeakiingigttaaarr http:/hhss.s///iiiieeeeeee orr//aaa-eeoott/ttrrrnnnnng drs pra-prellmiinry--e standaaar---adddds---a-aa- stw.dards-dddddddee for- for-generatiive-ai
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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 broader conceptual critique that they may oversimplify con- cepts that are intrinsically complex, discarding valuable mances Indexes may also be subject to gaming, which we discuss more extensively
99.2 LIMITATIONS
The litcrature and theory composite indexes much too extensive be casily summarized in this brief primer. We recommend the Handbook Constructing Composite Indicators: Methodology and User Guide (OECD al. 2008
proper introduction the subject: http:///ww....oo.......///////2.
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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. Strate- gically, our aim is for the index to clarify discourse on foundation models and AT that is muddled and lacks grounding in empirical data. We aim to improve the overall transparency the Al ecosystem by encourag- ing foundation model developers to share more information about the development and deployment of their models. We also provide clear laxonomization of the key issues related to transparency and demonstrate where greater transparency would be especially valuable. Therefore, the Foundation Model Transparency Index provides frame of reference for assessing whether the ecosystem as whole and which developers in particular-becmne 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 deci- sions in 99.2 LIMITATIONS) To guard against unnecessary simplification, we provide discussion and analysis at several levels of abstraction in
97 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 AT 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 reffect these changes. We more expansively discuss our intended impact (including our theory of change and associated limitations and risks)
in IMPACT
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4 Indicators
We define 100 indicators that comprchensively characterize transparency for foundation model developers. To select these indicators, we compiled relevant concepts raised across past. scientific literature well concerns animated by public discourse foundation models and other digital technologies. In Appendix B provide specific references for each indicator, and these references advocate for increased transparency and information sharing related to the indicator in question. We derived 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 [ indicators that are upstream of the model, indicators that relate to the model itsclf, and indicators that 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 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 mitigate data privacy and copyright concerns.
We depict the upstream indicators in Figure 3 Researchers have widely advocated for greater transparency
in relation to Data and Data Access [Bender & Friedman 2018a Gebru et al. 2018 Hutchinson et al. [2021;
Dodge et al.
2021 Bandy & Vincent 2021 as a means for contextualizing model capabilities (Sambasivan
et al.
2023) and risks related privacy, bias, and copyright (Buolamwini & Gebru Bender | 2021 Kandpal al. 2022 Sobel 2017]. Data Labor indicators uplift concerns related
2021 Longpre al.
2018
to labor practices, include irresponsible
exploitative
of human labor (Gray & Suri 2019a; Crawford
2021
Hao & 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 & Hernández Sarianndee-Grriia 2023:
Vipra & West 2023). Data Mitigations indicators also relate to the growing logal and sociotechnical concerns
over data privacy, copyright, and licensing (Henderson al. 2023
Brown et al. 2022 Lee et al. 2023a
Cooper al. 2023 Saveri et al. 2023
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2023 Foundation Model Transparency Index Indicators
Upstream
Model
Downstream
Input modality
decision-making
Data sources
Output modality
Release process
Data creators
Model componants
Distribution channels
Data source selection
Model size
Products and services
Data curation
Model architecture
Detection machine-genenn generated content
Data sugmentation
Centralized model documentation
Model License
Harmful data filtration
External model access protocol
Terms of service
Copyrighted data
Blackbox external model access
Permitted and prohibited users
Data license
Full external model access
Permitted, restricted, and prohibited
Personal information in data
Capsbilities description
Usage policy enforcement
Use human labor
Capsbilitiss demonstration
Justification for enforcement action
Employment data laborers
Evalustion of capabilities
Usage policy violation appeals mechanism
Geographic distribution of laborers
External reproducibility of capabilities evaluation
Permitted, restricted, and prohibited model behaviors
Wages
Third party capabiltiiss evaluation
Model behavior policy enforcament
Instructiona for creating data
Limitations dascription
Interoperability usage and model behavior policies
Labor protactions
Limitations demonstration
User Interaction with Al system
party partners
party evaluation
Usage disclaimers
Quaryable external data access
Risks description
User data protection policy
access
damonstration
prohibited
Compute usage
Unintentional harm evaluation
Usage data access protocol
Development
External reproduuciiiiity of unintentional harm evaiuation
Versioning protocol
Compute hardiware
Intentional harm evaluation
Change log
Hardware
External reproducibilityy intentional harm evaluation
Deprecation policy
Energy usage
Third party risks evaluation
Feedback mechanism
Carbon emissions
Mitigations description
Feedback
Brosder environmental impact
Mitigations demonstration
Government inquiries
Model
Mitigations evalustion
Monitoring mechanism
Model objectives
External reproducibility mitigations evalustion
Downstream applications
Core frameworks
Third party mitigations evalustion
Affected market sectors
Additional dependencies
Trustworthiness evalustion
Affected individuals
Mitigations for privacy
External reproducibility frustworthiness evaluation
Usage reports
Mitigations copyright
Inference duration evaluation
Geographic
Inference compute evaluation
Redress mechanism
for
Documentation for responsible downstream use
Figure 2: Indicators. The 100 indicators of the Foundation Model Transparency Index spanning the 3 domains: upstream, model, and Kownstream.
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Upstream Indicators the 2023 Foundation Model Transparency Index
Upstream
Data size: For the data used in building model, is the data size disclosed?
sources:
buildiin model,
sources disclosed?
Data creators: For all data used building the model, there characterization of the who created the data?
Data source selection: Are the selection protocols for including and excludling data sources disclosed?
Data curation: all data sources, are curstion protocols those data sources disclosed?
augmentation: any the developer takes augment data disclosed?
Harmful data filtration: If date filtered remove harmful content, there description of the associated filter?
Copyrighted data: For all data used in building the model, the associated copyright status disclosed?
license:
building the model, is the associated license status disclosed?
Personal information data: all used building the model, is the inclusion exclusion of personal information in that data disclosed?
Use of human labor: Are the phases of the data pipeline where human labor is involved disclosed?
Employment data laborers: the organization that directly employs the people involved in data labor disclosed each phase the data pipeline?
Geographic distribution of data laborers: is geographic information regarding the people involved in data labor disclosed for each phase of the data pipeline?
Wages: Are the wages for people who perform data labor disclosed?
Instructions for creating data:
instructions given people perform data labor disclosed?
Labor protections: Are the labor protections for people who perform data labor disclosed?
Third party partners: Are the third parties who were involved in the development of the model disclosed?
Queryable external data access: Are external entities provided with queryable access to the data used to build the model?
Direct external data
external entities provided direct the data build model?
Compute usage: is the compute required for building the model disclosed?
Development duration: Is the amount of time required to build the model disclosed?
Compute hardware: the primary hardware used to build model, amount type of hardware disclosed?
Hardware For the primary hardware used in building model, the the hardware disclosed?
Energy usage: is the amount of energy expended in building the model disclosed?
Carbon emissions: Is the amount of carbon emitted (associated with the energy used) in building the model disclosed?
Broader environmental impact: Are any broader environmental impacts from building the model besides carbon emissions disclosed?
Model stages: Are all stages the model development
disclosed?
Model objectives: For all stages that are described, is there clear description of the associated learning objectives or clear characterization of the nature of this update to the model?
frameworks:
frameworks used model development disclosed?
Additional dependencies: Are any dependencies required to build the model disclosed besides data, compute, and code?
Mitigations for privacy: Are any steps the developer takes to mitigate the presence of in the data disclosed?
Mitigations copyright: any steps developer takes mitigate presence of copyrighted information data disclosed?
Figure 3: Upstream Indicators. The 32 upstream indicators that span Data, Data Labor, Data Access. Compute, Methods, and Data Mitigations.
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4.2 Model indicators
The model indicators identify the properlies 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 modalitics, 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). Assosses transparency regarding the capabilities of the model, in- cluding evaluations.
Limitations (3 indicators). Assesses transparency regarding the limitations of the model, includ- ing 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.
We depict the model indicators in Figure 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 litcrature tied to the spectrum of model relcase and the associated differences in external ac-
cess (Solaiman et al. 2019 Sastry 2021 Shevlane 2022 Liang et al. 2022b Solaiman, 2023). The indicators on Capabilities, Limitations, Risks and Model Mitigations are motivated by a common understanding that these factors jointly influence the socictal impact of machine lcarning models and AT systems (Tabassi) 2023b;
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
38 To make these assessments more rigorous, the evaluation indi- cators build on the extensive tradition of evaluation in AI spanning iconic benchmarks like ImageNet (Deng] et al. ),09), broader benchmarks like SuperGLUE Wang et al. (2019). and extensive meta- meta-benchman benchmannchakkks 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 & Steinhardt
2019 Kapoor et al.
2023
Kapoor & Narayanan 2023 (39) and independent assessment
et al. 2022 Raji et al. 2022b Rajil 2022: Lam et al. 2022 Weidinger et al. (023)) which enable open science and external verifica- tion of developers' claims about their models. In the case of risks, finer distinctions between unintention
(anddvi et al. 2014 Raji & Buolamwini
2019:
Metaxa et al. 2021 Costanza-Chin
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 2023a Weidinger et al., 2023
Indicators on trustworthiness and inference are especially motivated by the Trustworthy ML Initiative and
ML.Prr (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.
202
Narayanan et al. 2023
38 See
gov. wk http:///www........ooooo/..o/tt.
sector
Sec the ML Heproducciilityy challenge: ittpes//paperawiihccod.. com.cc0020 CodaLab workshhetss for reproducible ML:
httpa: http://ooorrehetacccaaaaa codalab.......ooooo.. org// and Joelle Pincau's reproducibility checklist: httpp:///vwe cs. wwt.giill..a.ppinaaaueee
roduucibbiittyyceccliiss..ed
http:://wwww.uutttsrrtmm...... org/
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Model Indicators for the 2023 Foundation Model Transparency Index
Model
Input modality: Are the input modalities for the model disclosed?
Output modality: the output modalities for model disclosed?
Model components: Are components of the model disclosed?
Model size: For all components the model, is associated model size disclosed?
Model architecture: the model architecture disclosed?
Centralized model documentation: key information about the model included in a centralized artifact such model card?
External model access protocol: Is protocal for granting external entities access to the model disclosed?
access: black
access provided
Full external model accees: Is full model access provided to external entities?
Capabilities deseription: Are the model's capabilities described?
Capabilities demonstration: Are the model's capabilities demonstrated?
Evaluation capsbilities: Are the model's capsbilities rigorously evalusted, with the results of these evalustions reported prior to or concurrent with the initial release the model?
External reproducibility of capabilities evaluation: Are the evalustions of the model's capabilities reproducible external entitios?
Third capabilities evaluation: Are the model's capabilities evaluated by third parties?
Limitations description: Are the model's limitations disclosed?
Limitations demonstration: Are the model's limitations demonstrated?
Third party evaluation
evaluated by parties?
Risks description: Are the model's risks disclosed?
Risks demonstration: Are the modal's risks demonstrated?
Unintentional harm evaluation: Are the model's risks related to unintentional harm rigorously evaluated, with the results of these evalustions reported prior concurrent
initial the model?
External repraducibility of unintentional harm evaluation: Are the evaluations of the model's risks related to unintentional herm reproducible by external entities?
Intentional harm evaluation: Are the model's risks related to intentional harm rigorously evaluated, with the results of evaluations reported prior to concurrent with release model?.
External reproducibility of intentional harm evaluation: Are the evaluations of the model's risks related to intentional reproducible by external entities?
Third party risks evaluation: Are the model's risks evalusted by third parties?
Mitigations description: Are model mitigations disclosed?
Mitigations demonstration: Are the model mitigations demonstrated?
Mitigations evalustion: Are the model mitigations rigorously evalusted, with the results of these evalustions reported?
reproducibility of mitigations evaluation: the mitigation evaluations reproducible by entities?
Third party mitigations evaluation: Can the model mitigations be evalusted by third parties?
Trustworthiness evalustion: the trustworthiness of the model rigorously evalusted, with the results of these ovaluations disciosed?
External reproducibility of trustworthiness evalustion: Are the truetworthiness evalustions reproducible by external entities?
Inference duration evalustion: is the time required for model inference disclosed for clearly-speciffed task clearly-spacifidd set of hardware?
Inference compute evalustion: the compute for model inference disclosed for cpeariy-specified task clearly-speciffed set of hardware?
Figure 4: Model Indicators.
The 33 model indicators that span Model Basics, Model Access, Capabilities, Limitations, Risks, Model Mitigations, Trustworthiness, and Inference.
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4.3 Downstream indicators
The rownstream indicators identify the use of the loundation model, including details about ils 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. Addition- ally, this subdomain assesses the presence of model licenses, terms of service, and mochanisms for detecting model-generated content.
Usage Policy (5 indicators). Assesses transparency regarding the developer's acceptable use policy such as restrictions on specific uses users, well 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 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, 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 stored, shared, and accessed.
Model Updates (3 indicators). Assesses transparency regarding the developer's versioning pro- tocol, 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 subdo- main assesses transparency regarding downstream applications, usage statistics, and mechanisms for monitoring usage as well providing redress in the event of harm users.
Downstream Documentation (2 indicators). Assesses the presence of centralized documenta- tion for downstream use and documentation for responsible downstream use.
We depict the downstream indicators in Figure 5 Given that foundation models are the basis for down- stream supply chain (Bommasani et al. 2021 ) the distribution indicators are informed by the litcrature on
AI supply chains (Bommasani et al. 2023b Vipra Korinek, 2023 Cen et al., 2023 Cobbe et al.] 2023
Widder & Wong, 2023: Brown 2023 | and release practices (Liang, 2022; Solaiman 2023 Henderson et al.
2023 Kirchenbauer et al.) 2023 Kuditipudi et al. 2023 Liesenfeld al. 2023). Usage policy indicators draw
from company publications on responsible model deployment Coheree 2002 well precedents from social media. Model behavior policy indicators are rooted in literature that discusses AT behavior and trustwor-
thiness, 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 & Schulze (2023). User interface
indicators are derived from research on safety by design and hunan-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 5 Vipra & Myers West (2023); ; Winograd (0023); Nissenbaum (2024); King (2020); Mulligan el. al.
20166 Model updates indicators stem from work focused adequately updating systems and version con-
trol of AI systems (Sathyavagoesran et al. 2022 Hashesh 2023) Chen et al.) 2023b). For feedback, impact and downstream documentation, the indicators were motivated by the literalure on algorithmic auditing
Liang 20022:
Solaiman 2023 Raji et al. 2022b) as well as transparency reporting practices for social
media
https:: http////////////////////////////
http:://ranppraagrpppt...oo
com/ and http:s////raaaprrreectccccceaa/p
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Downstream Indicators for the 2023 Foundation Model Transparency Index
Downstream
Release pacislon-making: the developar's protocol for deciding whether release model disclosed?
Release process: description the process how model was released disclosed?
Distribution channels: Are all distribution channels disclosed?
Products and services: Does the devoloper disclose whother any products and services offered by the developer are dapendinnt the modal?
Detection machine-genorated content: any mechaniams detecting content generated by model disclosed?
Model License: is license the model disclosed?
Terms of services terms of service disclosed for each distribution channel?
Permitted prohibited users: description
model disclosed?
Permitted, restricted, and prohibitted uses: Are parmitted, reatricted, and prohibitad uses the modal disclosed?
Usage policy enforcement: the enforcement protocol for the usage policy disclosed?
Justification for enforcement action: Do users receive justification when they are subject to an enforcament action violating usage policy?
Usage policy violation appeals mechanism: mechanism for appealing potential usage policy violations disclosed?
Permitted, restricted, and prohibited model behaviors: Are model behaviors that are permitted, restricted, and prohibited diaclosed?
Model behavior enforeament: enforcement protocal for the model bahavior policy disclosed?
Interoperability usage interoperate disclosed?
policies: way usage policy and model behavior policy
User interaction with system: For distribution channels with user- facing interfaces, are users notifiied () that they are interacting with Al system, specific foundation they are interacting with, that outputs machine- getirattgg
Usage disclaimers: For distribution channels with user-facing interfaces, model use?
provided with disclaimers involving
User data protection pollcy: Are the protocols for how the developer stores, accesses, and shares usar data disclosad?
Permitted and prohibited use of user data: Are permitted and prohibited uses of user data disclosed?
Usage data protocol: is protocol for granting external entities to usage data disclosed?
Versioning protocol: there disclosed version and versioning protocal for the model?
Change a change log for the model?
Deprecation policy: Is there a disclosed deprecation policy for the developer?
Foedback mechanism: a foedback machanism disclosed?
Feedback summary: ls report or summaar disclosed regarding the feedback the developer received or, alternatively, the way developer responded
Government inquiries: summary government inquiries to received by the developer disclosed?
Monitoring mechanism: For each distribution channel, is monitoring mechanism for tracking model use disclosed?
Downstream applications: Across all forms of downstream use, the number of applications dependent on the foundation modal disclosed?
Affected markot sactors: Across downstream applications, fraction applications corresponding market sector disclosed?
Affected individuels: Across all forms downstream the number of individuals affected by the foundation model disclosad?
Usage reports: is usage raport that gives usage statistics describing the impact of the madel on users disclosed?
Geographic statistics: Across all forms of downstream use, are statistics of model usage across geographies disclosed?
Redress mechanism: is mechanism to provide redress to users for harm disclosed?
Centralized documentation for downstream documentation for downstream centralized in centralized
artifact?
Decumentation for responsible downstream use: Is documentation for reaponsible downstream use disclosed?
Figure 5: Downstream Indicators. The 35 downstrean indicators that span Distribution, Usage Policy, Model Behavior Policy, User Interface, User Data Protection, Model Updates, Feedback, Impact, and Down- stream Documentation.
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Note on assessment of indicators. We assess each indicator based 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 must explicitly point to the information in its docu- mentation. This implies that developers are overly vague or 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 specific action related to an indicator (e-g. they do not have a mechanism for users to provide feedback) then generally award 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 DACKGROOUD-TRRANSPARRNO existing conceptualizations transparency in AT often consider upstream resources (especially data) addition 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 lopic.
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 character- izing the downstream impact of foundation models may be challenging, especially for open model developers. By relcasing 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 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 socicty.
and impact. In addition, believe in the potential for greater
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5
Foundation model developers
Name
Flagship
Release
Input
Ouipat
Status
Headquarters
WHI
WH2
WHS
FME
A121 Labs
Jurassio-2
API
Test
Test
Stortup
Tel Aviv, Isnsel
Arazzn
Titan Text
API
Test
Text
Big Tech
Souttle, USA
X
X
X
Authie
Claude
Stactup
Sau Fraucisto, USA
Cohere
Command
API
Text
Text
Startup
Toronto, Canada
X
Google
Big
Mountain
Hugging Face
BLOOMZ
Open wrights, open data access (API forthoomingg)
Text
Text
Startup
Brooklyn USA
X
Inflection
Inflection-1
Text
Staclup
Palo Allo. USA
X
Meta
Llamaa
Open weights
Text
Text
Big Tech
Menlo Park, USA
OpenAl
GPT-4
Text, Inngge
Stactup
San Francisco, USA
Stability Al
Stabie Diflusion 2
weights, open data
Text
Images
Startup
London, UK
Table 1: Selected foundation model developers. Information the 10 selected foundation model developers: the developer name, their flagship model, the release strategy for the model (see Figure G), 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 Housc's initiative for public evaluation of AI systems announced in May 2023 (WH1), voluntary commitments for the management of risks posed by AT announced in July 2023 (WH2), and commitments by additional organizations on the same matters of risks by Al 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 Al (e.g. datasheets and model cards) often introduce frameworks that support machine learning developers in achieving greater transparency in their own work. In contrast, 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 el al. 22018) 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 rich context for how improve transparency in the foundation model ecosystem.
Efforts like Ecosystem Graphs (Bommasani al. 2023b) and the UK Competition and Markets Authority (CMA) report on the foundation model market" 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 43 However, as the CMA report states, a small number of developers control the majority of the market at present (Vipra & Korinek 2023). Due to this intense level of market concentration, we decided to assess 10 major foundation model developers.
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5.1 Selecting developers
We considered variety of selection criteria in choosing the 10 developers to assess, arriving at the following three principles:
Impact. We selected developers that have built the most influential foundation models.
2. Diversity. We selected developers that, when considered collectively, represent many axes of varia- lion in the foundation model ecosystem. For example, developers that. release models along different points on the release gradient (e.g. open vs. closed, [Soaiman 2023 build models with different modalities (e.g. text-to-text vs. text-to-imago), and occupy different positions in the market (e.g. startups vs. Big Tech).
3. Companies. We selected developers that are established companics as enduring targets for lon- gitudinal improvement. This to some extent parallels current. regulatory initiatives that explicitly focus on companies as the target of policy for foundation models
On this basis, we chose 10 companics 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 modality (e.g. Cohere, OpenAl, 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 45 a set of voluntary com- mitments announced in July 2023 16 and another set of voluntary commitments announced in September 2023 Separately, three of the companics we assess jointly announced the formation of the Frontier Model Forum July 2023 IS When taken logether, these announcemnnts name 16 companies: Adobe, Amazon, Anthropic, Cohere, Google, Hugging Face, IBM, Inflectinn Meta, Microsoft, NVIDIA, OpenAI, Palantir, Salesforce, Scale AI, and Stability AI. We note that of the 10 companics we selected are within this set of 16 (all but A121 Labs).
The gradient of release strategies. The stratogics for releasing foundation models differ widely (sce Figure Gp. 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, Al 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
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ation by-
wwt.er/wwteeeeattattt.taaaaaaaat s--ooed-by-ai/
fdel-forum/
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internal research only high risk control low auditability limited perspectives
community research low risk control high auditability broader perspectives
gated to public ..
Lovel of Access
fully closed
gradual/staged release
hosted clouc-based/APP
downloadable
fully open
PaLM (Google)
GPT-2 (OpenAJ)
DALLE 2 (OpenAl)
GPT-3 (OpenAl)
OPT (Meta) Cralyon (cralyon)
BLOOM (BigScience) GPT-J (EleutherAl)
Spstem (Devalopee)
Gopher (DeepMind)
Stable (Stbiit Al) Midjourny (Midjoumey)
Imagen (Google)
Make-A-Voi A-Video (Meta)
Figure 6: The gradient of relcase 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 access the model's outputs), released via programmatic API (c.g. users query the model and receive outputs programmatically)) released via downloadable model weights (e.g. users access and adapt the model), 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 (Iuflection-l), though Inflection plans to make available via an API; six are available via an API (GPT-4, Claude 2, PaLM 2, Jurassic-2, Command, Titan Text); one 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 al times binarize these distinctions into models with downloadable weights ("open") and models without downloadable weights ('closed"). Image taken with permission from Solaiman 2023
access, as Midjourney does for its text-to-image models that it makes available via a Discord server 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-Vides 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, Authropic, Cohere, Google, Inflection, and OpenAI.
49
The overall approach to release is informed by developer's business strategy and perspective 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, OpenAl did not disclose many details about the modeling architecture and training data, citing competition and safety as the two main reasons 60 On the other hand, when releasing the text-to-speech Whisper model Radford et al. (2022), OpenAl disclosed many details and released the model weights openly. For other developers, the release decision may directly relate to their purpose for building foundation model in the first place. For example, the BigScience collaboration led by Hugging Face that led to the BLOOM model (Le Scao al.) 2022) explicitly designed to democratize access to multilingual large language models with capabilities in traditionally underrepresented languages. As result, the initiative released model weights and data.
49Scc Interview with OpenAl's chiel scientist and co-lourder:
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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. OpenAT releases the weights openly for some of these models but not others), we decide assess developers in relation to their flagship foundation model. By flagship foundation model, 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 's public description of the model. We provide basic information about each of the developers and their flagship model in Table 51
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 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.
51 For OpenAl, evaluate GPT-4, which released in March 2023, GPT-4V, model OpenAl released in September 2023 after we completed our analysis. With respect input and output modality, OpenAl 2023 states that GPT-4 is "a large multimodal model capable processing image and text inputs and producing outputs."
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6 Scoring
By selecting the indicators and companies, we abstractly specify the form of the index. By defining cach 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 avail- able 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 pub- licly 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 structured search identify all relevant information that is public. The exact details of how execute this search are provided in Appendix C
Initial scoring. Having identified the information basis for scoring indicator, 2 researchers on the team independently scored the developer on the indicator. This entails specifying score (i.e. O 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 the sharp distinction between 0 and 1 point for each indicator. Second, narrow criterion for making 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 al. 2023a 52
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-graine grained interpretation of how to score an indicator. Overall, across all 100 x 10 (indi- cator, developer) pairs, the agreement rate was 85.2% (Cohen's x 0.67, indicating substantial agreement: Landis Koch 1977). To resolve disagreements, the rescarchers discussed and jointly came 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.
52gee 992 LIMITATIONS for further discussion.
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Company feedback. Given that these scores constitute direct assessment of specific companies, 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, 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 companics, all 10 responded. Of these, 8 companies (Amazon, Anthropic, Cohere, Hugging Facc, 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 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 public written response regarding their perspective on this initiative, their specific scores, and their broader approach as organization to transparency and responsible AT it relates to foundation models. Moving forward, we hope these organizations implement more transparent practices and we provide specific recommendations to that effect in 88.1 RECOMMENDATIONS DEVELOPERS
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Foundation Model Transparency Index Total Scores, 2023
Source: 2025 Foundation Model Transparency Index
Company
Score
O Meta
Liama 2
54%
BLOOMZ
53%
OpenAI
GPT-4
48%
stability.ai
Stable Diffusion
47%
Google
PaLM 2
40%
ANTHROPIC
Claude
36%
cohere
Command
34%
A121 labs
Jurassic-2
25%
Inflection
Inffection-1
21%
amazon
Titan Text
12%
Figure 7: Overall Scores. The overall Foundation Model Transparency Index score and ranking across all 100 indicators.
7 Analysis
The finalized results of the Foundation Model Transparency Index are the scores for each of the 100 indicators across all 10 companics. These result are accessible at https:://ww.giit http://www.gthhu..bpb cactuub..om//s/ www.coo/scaaa. hamm/ttanford---
ffttm 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/ commnnniesssss (e.g. release strategy), as well as data-driven trends (e.g. correlations).
7.1 Overarching results
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 findings these overarching trends with more granular consideration of the major dimensions of transperency in the index in Figure
down into the 3 domains (upstream, model, downstream; Figure 8 We supplement
All developers have significant. room for improvement. But most transparency indicators are very obtainable, having been implemented by at least one developer. Based 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 clear reason to believe that across all developers, the necessary change become more transparent feasible. That companies' competitors are more transparent in certain issue areas suggests that such transparency, unlikely to cause serious damage to their business. Companies emulate the higher level of transparency their competitors exhibit certain indicators, providing a precedent and starting point for improving transparency in the foundation model ecosystem.
if not fully costless, is
53thhe major dimensions transparency highlight are large subdiomains among the 23 subdomains.
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Foundation Model Transparency Index Scores by Domain, 2023
Source: 2025 Foundation Model Transparency Index
Meta Llama 2
Hightiene
BLOOMZ
OpenAl GPT-4
48
stablityai Stable Diffusion
Google PaLM 2
40
ANTHROPIC
Claude 2
coheze
Command
Al21labs Jurassic-2
25
Upstream
Inflection Inflection-1
Model
Downstream
amazon Titan Text
12
20
40
60
Score
Figure 8: Scores by Domain. The aggregate score of each developer broken down by the three domains:
upstream, model, and downstream.
Foundation Model Transparency Index Scores by Major Dimensions of Transparency, 2023
Source: 2025 Foundation Model Transparency Index
00 Meta
Migicbara
OpenAl
stability.ai
Google
ANTHROPIC
AI21
Inflection
amazon
Liama
BLOOMZ
GPT-4
Stable Diffusion
P
Claude 2
Command
Juressic-2
Inflection-1
Titan Text
Average
Data
20%
20%
0%
20%
0%
0%
Compute
67%
14%
14%
57%
14%
0%
54%
0%
0%
0%
17%
Methods
50%
75%
0%
Major Dimensions of Transparency
100%
100%
50%
83%
67%
67%
50%
33%
50%
33%
63%
Model Access
100%
100%
67%
100%
33%
33%
67%
33%
0%
33%
57%
Capablities
100%
80%
60%
Risks
57%
0%
67%
14%
29%
29%
29%
29%
0%
0%
24%
Mitigations
60%
0%
60%
0%
40%
40%
20%
0%
20%
20%
26%
Distribution
Usage Policy
40%
20%
80%
40%
60%
60%
40%
20%
60%
20%
44%
Feadback 33%
33%
33%
33%
33%
33%
33%
30%
Impact
Average 57%
82%
47%
47%
41%
39%
31%
20%
20%
13%
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.
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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, OpenAT, Stability AT) all cluster well above the mean, the next three are very close to the mean (Google, Authropic, 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 (o.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 g 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 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 B), with Cohere receiving 3 points and all of A121 Labs, Inflection, and Amazon receiving 0 points. In contrast, for both the model and downstream domains, all 10 companics 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 Al), around (Google, Anthropic, Cohere), or well-below (AT21 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, OpenAl 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 Al with 16). In particular, OpenAT and Stability AI receive the nearly the same overall score, with OpenAl 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, Authropic, and Cohere, the discrepancies are less stark, but we do see that Cohere is at 3 in the upstream category compared to Google with 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 (.66 CORRELATIONS Among the three lowest-scoring developers, model domain, with both scoring a zero on the upstream domain and similarly on the downstream domain.
see that A121 Labs and Inflection are differentiated by the
Data, Data Labor, and Compute are pervasive blind spots across developers. While the overall and domain-level results provide basic lay of the land, we find that the major dimensions of transparency provide the Goldilocks region for clear and incisive analysis shown in Figure In particular, these dimen- sions of transparency that are tied to broadly-mddestaddable 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, Foedback, 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;
subdomains with several indicators (so the subdomain scores are more reliable)
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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 includ- ing data, labor, compute, mothods, 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 human laborers and the environment). As we show in Figure g the upstream indicators
(e.g. impacts are the most sparsely awarded (22.5% coverage average). Here, 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 range of 21 when compared to the lowest- scoring developers: A121 Labs, Inflection, and Amazon (0 of the available 32 indicators; 0%). We emphasize this striking disparity given that many of the fundamental socictal 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 volunteors providing data. Developers generally share no information about the use of human labor in their data pipeline, the employer, wages, and geographical dist.ribution 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 & Suri 2019a
Crawford
2021
Hao & Seetharaman 2023
The Compute subdomain shows major discrepancies among developers. Meta and Stability AT 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 & Gernández-Garcia 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
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Foundation Model Transparency Index Indicator-Level Scores for Upstream, 2023
Source: 2023 Foundat Model Transparency Index
DoMero
OpenAl
stailityal
Google
A121 Jarmes
Inflection
amazon
Lirr
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Compute hardware
Energy ssag
Garan univacrs
Modld dbsectivs
Mathods
Adcitoral cependencies
Mitigtioos
Milgricee
copyriht
Upuinemn Subtotel
44%
85%
22%
50%
10%
10% 9%
0%
0%
0%
Figure 10: Upstream Scores by Indicator. The scores for each of the 32 upstream indicators.
usage may be of similar consequence environmentally (Luccioni & Hernández- Garcia 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 derived from BLOOM, we note the potential for documentation transience, where prior documentation is not updated refloct 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 disclosed.
Widespread lack of upstream transparency on data crcators, 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, 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 these indicators is issue in ongoing litigation. In particular, Stability AI has explicitly argued that training foundation models on copyrighted data is protected by fair doctrine in the U.S 5T Closed developers may also view information related to their data key competitive advantage, legal risk. Additionally, we note that we are surprised no developer directly discloscs the compute usage in FLOPs to sufficient precision, though several disclose information that could be used to compute an estimate or upper bound.
be disincentivized to share this information due to perception of
54see http:///wwddacaa.e.eeeeeee////// http:///ww.s..toooo.....///////r _etstiionnn..... brooks. pdf and https: www../dwennccoo..ooe//oooeeceee /tw. well as Lemley & Casey (2020).
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Foundation Model Transparency Index Indicator-Level Scores for Model, 2023
Source: 2023 Foundation Model Transparency Index
OpenAl
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Google
amaczo
Limre
BLOOME
4
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67%
48%
67%
39%
45%
40% 40%
30%
21% 10%
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 no upstream indicator for which every developer receives points. Of course, this guaranteed by the presence of (multiple) developers that score points on the entire upstream domain. Even putting these 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 or more developers score no points on 30 of 32 upstream indicators, as strong evidence that upstream transparency 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 informa- tion about the model. The indicators in this domain comprehensively characterize the foundation model as standalone artifact: what tasks the model and cannot. perform, what is the model's basic structure, who has access to the model, and more. Here, analyze developers at the level of subdomains and indicators
based on Figure 11
Model subdomains are some of the highost-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%), Capabilitics (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).
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Transparency on capabilities does not translate to transparency on limitations, risks, miti- gations. 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 capabilitics, with the majority reporting evaluations (6 of 10), half reporting reproducible evaluations (5 of 10), and few pro- viding 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 assume most 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 indi- cators than 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 capabilitics 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 A121 Labs provide information about rigorous evaluations of their flagship model related to robustness, reliability, hallucinations, calibra- tion, or explainability. Of those developers, only Cohere and AT21 Labs provide sufficient detail for their ovaluations to be deemed externally reproducible due to their use of the HELM benchmark (Liang et al., 2023), compared to OpenAl's unclear description of their evaluations of model calibration. Given the previ- ous 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 overre- liance on foundation models because not enough is done to calibrate consumers on the appropriate levels of trust (Parasuraman & Manzcy 2010) 55 With this said, we do acknowledge that developers may take other routes lowards improving trustworthiness including methods like reinforcement learning from human feed-
back (Ziegler et al. 2019 Ouyang et al., 2022 and constitutional AI Bai et al. 2022). though transparency
is lacking on these approaches (CCaspr et al. 2023).
Model Access reveals slight. differences beyond just release strategy. Tn 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 Al release their models openly in terms of both model weights and data, whereas OpenAl is considerably more closed, providing only API access. However, in particular, OpenAl 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 (c.g. in comparison to OpenAl'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.
ility as an example.
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Foundation Model Transparency Index Indicator-Level Scores for Downstream, 2023
Source: 2023 Foundation Model Transparency indox
coMeto
OpenAAl
staliital
Google
A12llabe
amazon
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6%%
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60%
51%
54%
40%
43% 40%
40%
17%
Figure 12: Downstream Scores by Indicator. The scores for each of the 35 downstream indicators.
Model Mitigations are 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 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 evaluated. Even when mitigations are described, in scoring find the mapping between stated risks and stated mitigations is often vague 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.
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 model indicators. The 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 additional indicators for which only one developer scores points limitations demonstration (Cohere) and external reproducibility of internal harm evaluation (OpenAT). 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 foundation models (Narayanan et al. 2023) given the key role of efficient inference and low latency the usability of models (Lee et al.) (022b)). We see that BLOOMZ in particular provides potential benchmark for language models by tracking the time spent for fixed task (generating 100 tokens given token prefix) fixed hardware (a NVIDIA A100-80GI GPU), though compute is not measured 56
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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 the model, user interfaces, user data pro- tection, model updates, feedback, impact, and documentation. Indicators in these subdomains characterize transparency related to how the foundation model is deployed and its downstream effects the ecosystem and society. Our analysis is based on publicly available information about how the foundation model is distributed, how il 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 wetween 14 and 21 points. The highest-scoring on the downstream domain is OpenAl at 21 points and the lowest- scoring (barring Amazon) are A121 Labs and Inflection at 14 points. In (76 CORRELATIONS we clarify the extent to which these smaller margins in scoring discrepancies in the downstream domain are due to high agreement 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 developers scoring points on just of the possible indicators and the remaining 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 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 37
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 Distri- bution 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 or- ganization. Every developer provides terms of service that cover the distribution of its foundation model 58 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.) 2022a Liang& & 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,
57 score the downstream applications indicator quite generously: all of the open developers score points because they discloses which Hugging Pace 'Spaces' also using the model via Hugging Face's platform. However, emphasize that this is still poor proxy for the number of applications dependeut on the foundation modcl. 58 As with several downstream indicators, we assessed the terms service of the primary distribution channel. For example, this meant that we assessed Microsoft Azure's terms of service for Meta.
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we highlight that many of companies assess have written about the broader topic of release, but not in way that is precise to their specific decisions for their flagship models 59
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 (OpenAl 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 usage policy indicators. OpenAT, Authropic, 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. OpenAl and Authropic 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 AT) 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, 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 leedback 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 function of how nascent the foundation model ecosystem is: companics have only been collecting feedback for 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 60
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 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.
5eW note that following September 15, 2023, Anthoopic released information about its approach responsible scaling: https://www.adt http:///wwwaattopi....oo httpaa//eeeeattrrrrrrrreeeeeeees -FForr example, consider the EU's DSA Transparency Database, implemented on the basis of the Digital Services Act to provide transparency on content moderation decisions: htttp:://tranpparens dsa.ec..eee...eeeeoooooo.. europaa
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Developers score well on the User Interface subdomain, though this may change due to de- ployments 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 user interface to assess these indicators. However, Amazon did not have a publicly available user interface in ad 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 OpenAl 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 Authropic does not make clear that user is interacting with an Al system, except for the textual description "Message Claude.'
7.5 Results for open and closed developers
Foundation models are released by different developers using a variety of release strategies (Liang et al. 2022a;
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., 2022a Liang & Reich 2022 Solaiman 2023 Widder
et al. 2023 Seger et al. 2023 To clarify how transparency differs between the open developers we assess Meta, Hugging Face, Stability AI) and the closed developers (i.e. OpenAI, Google, Anthropic, Cohere,
(i.c.
A121
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 (OpenAl): Meta and Hugging Face are at least 5 points higher, and Stability AT is within a point of OpenAT. 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.c. 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 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 hypothesos 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 de- velopers 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
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Average Transparency Open vs. Closed Developers By Subdomain, 2023
Source: Foundation Transparancy Indix
Data
47%
Data Labor
43%
6%
Data Access
0%
Compute
6%
Methods
29%
Data Mitigations
7%
Basics
50%
30%
Capabilities
Limitations
67%
Risks
33%
Model Mitigations
20%
Trustworthiness 0%
36%
Inference
33%
Distribution
53%
Usage Policy
49%
Model Behavior Palicy
29%
User Interface
79%
User Data Protection
67%
Updates
48%
Feedback
Open
Impact
Documentation Deployers
67%
0%
10%
20%
30%
40%
50%
60%
70%
80%
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Average (%)
Figure 13: Open vs. Closed by Subdomains. The mean score for the 3 open developers (Meta, Hugging Face, Stability AT) and the closed developers AT, Anthropic, Google, Cohere, AT21 Labs, Inflection, Amazon) across each of the 23 subdomains. Note: the number indicators per subdomain varies widely.
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may be higher-resoureed or face stronger incentives to proactively address certain matters around responsi- ble AT (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 loundation model, providing users of their platforms and clients who license their proprictary foundation models with an opportunity to push for transparency.
The mean score of open developers is higher than closed developers 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 espocially 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 devel- opers. At the indicator level, the median open developer outscores the median closed developer on 28 indicators (18 upstream, model, 3 downstream), while the median closed developer scores higher on just 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 un- derweighted 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, essarily based on the practices of some of the highest-resoureed open and closed developers, these trends should not be taken as sulficient 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 RECOMMENDAATIONSSDEELLLEEEE
caution that our assessment is nec-
7.6 Correlations between companies
Mcasuring correlations. The 100 x 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-compan
similarity for simplicity. For example, if two companies receive similar aggregate scores, 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 indicates that for all indicators both companics 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, consider three patterns in the correlation matrix: (i) individual cells with very small or very large values (i.c. highly similar or highly dissimilar company pairs), (ti) 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 few regions of the correlation matrix. First, we identify the three most correlated pairs: (Cohere, AT21
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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 (L.e. agreement rate), which is the fraction of all indicators for which both companies receive the same score (Le. both receive the point or both do not receive the point).
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Correlations between Companies (Upstream Indicators). The correlation between the scores for pairs of companies across all indicators when only considering upstream 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.c. both receive the point or both do not receive the point).
Figure 15:
Labs; SMC 0.87), (A121 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 these pairs, if consider the other highly-correlated pairs (SMC 0.8), identify: (Hugging Face, Stability AI; SMC 0.80), (OpenAI, Anthropie; SMC 0.80), and (Google, Authropic; SMC 0.80). In particular, clear structure: Hugging Face and Stability AI are the only two developers to release both data and models openly, and the trio of OpenAl, Google, and Anthropic are the three members of the Frontier Model Forum that we assess.
observe that the company correlations identify
Weakly correlated company practices. In contrast, we see that the least correlated pairs (SMC < 0.6) are pairs involving Meta and the three lowest-scoring developers as well as pairs involving Hugging Face and five of the seven closod developers (OpenAI, Cohere, AT21 Labs, Inflection, Amazon). These are all pairings between an open and closed developer. More broadly, we highlight that Meta the sole developer that is not correlated with SMC at least 0.80 with any other developer, with the most similar other developer being Google at 0.78 (see below for further analysis). This means Meta is rather unique in terms of the indicators where it scores points; it is the sole developer that is not strongly correlated with any other company, even including the two other open developers. Nevertheless, the least correlated pair of companies still agrees over half the indicators (SMC- = 0.54), which is not surprising given that all the companies are opaque (e.g. if all the companics all scored 0 on every indicator, they would necessarily be perfectly corrclated with SMC 1).
Upstream correlations. In Figure 15
we plot the correlation between every pair of companies when considering only indicators from the upstream domain. Since the four lowest-scoring companies overall
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Figure 16: Correlations between Companies (Model Indicators). The correlation between the scores for pairs of companies across all indicators when only considering model indicators. Correlation 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 both do not receive the point).
also score zero (or ncar-zero in the case of Cohere) points the upstream indicators, they necessarily extremely correlated. For the same reason, the extent to which the remaining six companies are correlated with the three lowest-scoring companies is precisely proportional to their own opacity on the upstream domain. Looking at the three companies that score in the middle overall (Google, Anthropic, Cohere), we see their indicator-level transparency reasonably correlated. We also see a similar trend where OpenAI, Google, and Anthropic are correlated, though this case OpenAT and Cohere are even more correlated with an SMC of 0.81. Interestingly, while the three open developers score much higher overall than any of the seven closed developers for the upstream domain, the correlations between them somewhat different than in the other domains: there is weaker correlation between Hugging Face and Stability AI, and Meta's correlation with OpenAT and Stability AT is stronger than its correlation with Hugging Face. Despite the fact that Meta and Hugging Face are the two highest-scoring companies on upstream, they are not especially correlated (SMC- = 0.53) in that domain. These discrepancies coincide with the indicators where Hugging Face scores points and the other two open developers (Meta, Stability AT) do not, namely those relating to data sources and data labor. Given the large spread in scores across developers in the upstream domain, we see the related effect that the correlations can be quite variable with some at or near and others well below 0.5 (minimum upstream SMC 0.34).
Model correlations. In Figure 16 plot the correlation between every pair of companies when con- sidering only indicators from the model domain. In contrast to the upstream correlations, more varied picture. First, much like the overall correlations, we see strong correlations for (Cohere, AI21 Labs; SMC = 0.85) and (Inflection, Amazon; SMC = 0.85) but not necessarily for the other pairs between these four companies. Among the three Frontier Model Forum companies, we see a very strong correlation of 0.88 between Google and Anthropic, fairly high correlation of 0.79 between OpenAI and Anthropic, but considerably lower correlation for the third pair OpenAI and Google at 0.67. These trends, where Anthropic is highly correlated with both, but OpenAT and Google not necessarily correlated, mirror what
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Figure 17: Correlations between Companies (Downstream Indicators). The correlation between the scores for pairs of companies across all indicators when considering only downstream 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).
we observe for the overall correlations. Similar to what we observed for the overall correlations, Hugging Face and Stability Al are quite correlated as well with a correlation of 0.85, and Meta not particularly correlated with any company (the highest is Hugging Face 0.76).
Downstream correlations. In Figure 17 we plot the correlation between every pair of companies when considering only indicators from the downstream domain. The downstream correlations surface considerably different trends from the overall correlations or those for the other two domains. In particular, first highlight that Meta is strongly correlated with Google in their scores on downstream indicators. Given that several of the downstream indicators related to broader corporate practices, the similarities between these companics may contribute to this result, though both companies are not strongly correlated with Amazon, the other Big Tech company we assess. Relatedly, we see fairly strong correlations between OpenAT and Anthropic, which again may relate their fairly similar business practices mapping onto specific downstream indicators (e.g. indicators in the Model Behavior Policy subdomain). On the other hand, akin to the upstream subdomain, (Meta, Hugging Face, Stability AT). And, unlike the other corrclation matrices, OpenAT and Amazon are more dissimilar than usual. Overall, while we do not observe it as clearly in the other correlation analyses, here we see all three pairs of open developers are highly correlated: (Meta, Hugging Face; SMC = 0.89), (Meta, Stability AI; SMC = 0.83), (Hugging Face, Stability A1; SMC = 0.89). This may reflect that all open developers have shared transparency challenges on specific indicators within the downstream domain (c.g. monitoring mechanism and model behavior policy enforcement), perhaps stemming from the weaker control they have over downstream use. Overall, we find the complex structure and heterogeneity in the correlation for the downstream domain especially intriguing, given the aggregate scores for this domain are the most tightly clustered (see 77.4 DOWNSTREAM RESULTS). That is to say, disaggregated indicator-level analysis is especially revealing for this domain compared to domain-level analysis.
that Inflection is especially dissimilar from all of the open model developers
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8 Recommendations
The design of our indicators, execution of our assessment, and analysis of our results provide rich supply of ideas for how to improve transparency. We center attention on foundation model developers and deployers, along with policymakers. For each group of stakeholders,
provide concrete recommendations the basis of this research. Additionally, we encourage researchers to scrutinize our overall approach in order to clarify how transparency for foundation models can be better understood in the future.
8.1 Recommendations for foundation model developers
By directly scoring foundation model developers, we provide explicit feedback where developers are and are not transparent. In itself, this provides immediate guidance for these 10 companies in the context of their current flagship foundation models. Moreover, the Foundation Model Transparency Index provides valuable insights for these companies to consider related to their other models and future releases; it also has bearing on how foundation model developers that we did not assess can promote transparency. We provide 10 specific recommendations for foundation model developers.
1. Increase transparency for existing foundation models.
As our results show, the development and use of major foundation model developers' current flagship models is opaque. Developers should remedy this situation by releasing more information about the systems that are central to today's foundation model ecosystem. Inereasing the transparency of future releases will not resolve this issue as there are hundreds products, services, and other models that are built on top of today's flagship models 61
Developers should begin by focusing on low-hanging fruit, such as clarifying ambiguous language in their documentation, centralizing existing information sources, and sharing information about models that poses minimal concerns related to market competitiveness or legal risk. 62 Developers should also be clear about why they will not release certain information about their foundation models; developers should explicitly state the subdomains where they do not release information and explain why they do not do
2. Increase transparency for future foundation model releases.
Developers should substantially increase the transparency of future foundation model releases. Wher- ever possible, they should publicly disclose information related to the 100 indicators outline well as additional information they feel is important to share with the industry, the public, and governments. This might look like taking a transparency-firy approach in which the developer pri- oritizes transparency throughout the model development process and includes transparency as an important performance metric for research teams 03
61 Developers that signed on to the White House's first round of voluntary commitments (including Amazon, Anthropic, Google, Inflection, Meta, and OpenAT) have pledged only to improve transparency "for all new significant model public releases within scope," where the scope defined as 'gennerativ models that are overall more powerful than the current industry frontier (e.g. models that are overall more powerful than any currently released models, including GPT-4, Claude 2, PaLM 2. Titan and, in the case of image generation, DALL-E 2)." Developers that signed on to the White House's second round of voluntary commitments (including Cohere and Stability AI) have pledged only to improve transparency for 'generative models that are overall more powerful than the current most advanced model produced by the company making the commitment. Bnts//nnnuuunnnnneeeeeeeee Saf---eecro--and -ewettteetttttteerrrrrrr Al-Comiiiii-mmmmeee tments September- 202e pdf
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o2For example, Anthropic released signifiantly more mitommaion about Claude than its previous Hagship model, Claude, including in the form of model card.
63e relevant analogy to the development of open foundation models. Much some developers begin the process of building foundation model with the intention of making all model asscts openly available, then subscquently decide if the risks of making model asset openly available outweigh the potential benefits, developers could begin the development process with the assumption maximum transparency and remove only some items along the way (Klyman 2023
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Profit-oriented developers commonly argue that certain forms of transparency endanger their competitive advantage. Nevertheless, developers have a basic responsibility to weigh this concern against the the risks posed by their technology to society and the benefits of increasing societal understanding of this technology via transparency. These risks should determined by not only the developer but also the assessment of third party experts. Voluntary access for independent, third party audits (i.c. auditors not selected by the developer itself), can achieve a greater degree of trans- parency, and safeguard competition concerns with non-disclosure agreements. We would also argue audits not always good substitute for public transparency, and developers' arguments around competitive advantage should be carefully assessed for each indicator of transparency. These argu- ments are a common refrain to avoid meaningful community discussion about widespread practices that do not in actuality endanger competilive ad advanlages.
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3. Follow industry best practices with respect to transparency.
Our lindings suggest that every developer could siguificantly improve transparency by drawing on different approaches to transparency from across the industry. At least one developer scores points on 82 of 100 indicators: where developers are struggling to increase transparency in specific issue area, they should look to developers that have already done so.
While the foundation model ecosystem is nascent, some developers have outlined best practices for responsible development that relate to transparency. For example, in their 'Joint Recommendations for Language Model Development, OpenAl, Cohere, and A121 Labs state that developers should 'publish usage guidelines and terms of use ... document known weaknesses and vulnerabilitics [and] model and use-case-specifie safety best practices." (See Appendix D for additional examples of calls from developers for transparency.)
4. Work with deployers to increase transparency.
In cases where developer is not the sole deployer of foundation model, the developer should partner with deployers to increase transparency. For example, developers should attempt to require that deployers disclose usage statistics and provide usage disclaimers. Developers might do so through legal agreements that they sign with deployers that grant deployers the right to offer the foundation model. If developer has little leverage over larger deployers it should consider partnering with similarly situated developers to increase their collective bargaining power. Without such efforts, it may be difficult for developer to assess the downstream impact of its foundation models.
5. Work with downstream developers to increase transparency.
Foundation model developers should make it easy for downstream developers to be transparent in thoir release of finc-tuned modols. addition to increasing transparcncy for their own models, foundation model developers should release documentation to help downstream developers be more transparent and actively encourage them do so.
6. Work with regulators to increase transparency.
While we believe that the public is entitled to information about each of the indicators of trans- parency that we examine, we recognize that it is unlikely that every foundation model developers will publicly release all of this information. In some cases, foundation model developers may argue the risks of disclosing such information are too great to justify public release. In many such cases, developers should still share this information with regulators such that governments have sufficient information adequately scrutinize developers the public interest.
7. U se transparency to improve trust, safety and reliability.
Sharing internal practices, documentation, and details about risks lead to short term criticism and negative media coverage, but in the long term it can foster greater community trust than is possible with a more opaque approach. Investigative journalists will eventually expose practices that lead to systemic harms, and these harms are often exacorbated the longer they remain hiddon, as illustrated by the Facebook Files Hagey & Horwitz (2021). Foundation models are technologies that could cause widespread harm, and the evidence suggests that safety and reliability will require dedicated and strong forms of transparency from foundation model developers.
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8. Dedicate resources to continue improving transparency over time.
As technologies and risks rapidly evolve, the varielies of and baselines for meaningful transparency will also change. Well-resourced developers should dedicate personnel adapting their documen- tation and releases to take account of this shifting landscape, rather than adhering to static bench- marks. Low-resourced developers should seek out funding in order to similarly improve transparency.
9. Work to improve transparency in the foundation model ecosystem.
There are many areas where transparency is sorely needed, ranging from the downstream impact of foundation model releases to the use human labor in producing the data used to build foundation models. One cross-cutting issue the fact that developers do not exist models a developer releases depend on and significantly affect other parts of the ecosystem. Taking this into account, developers should increase transparency a means of improving the health of the overall ecosystem.
vacuum: the foundation
Developers should use semantic versioning for their models (as is the norm in software engineering) such that there is no ambiguity as to the version of the model that is being distributed. Developers should also give as much notice as is practicable (e.g. months notice) in advance of deprecating models order to give any downstream dependencies adequate time to migrate to a new version.
Developers should release artifact alongside their foundation models that includes information about models' upstream and downstream dependencies (Bommasani et al. 2023b). Information about the datasets, software frameworks, and applications the model depends upon, as well as products, services, and other models that depend upon the model, are essential for effective supply chain monitoring.
10. Use the Foundation Model Transparency Index to increase transparency.
The Foundation Model Transparency Index provides an extensive taxonomy of the key elements of transparency in the field. We encourage developers to score their non-flagship models on the index and see where they have room for improvement.
Each indicator contains significant detail that developers can utilize to increase transparency in spe- cific issue areas. For quantitative metrics, indicators include information regarding the appropriate uiit of measurement and level of precision. For qualitative metrics, indicators often provide de facto instructions for how to clearly share information about specific subdomain with the public.
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8.2 Recommendations for foundation model deployers
Foundation model developers are not the only aclors with responsibility lo promote transparency: deployers of foundation models such as cloud services providers and companies that license foundation models from developers also have a significant rolc to play. Although deployers cannot unilaterally increase transparency as they are not the party responsible for building a foundation model, there are still some tools at their disposal for doing so and they should think seriously about the implications of relying on systems for which there is little publicly available information.
1. Assess the risks of deploying foundation model without adequate transparency.
Deployers that make use a developer's foundation model in their products and services should conduct pre-deployment risk assessments that include specific assessments of risks stemming from a lack of transparency. These risks may include increased legal liability for diffieul-to-explain model behaviors, reduced trust from users due to the product's opacity, and lower product performance without adequate information about the data used to build the model.
2. Require sufficient transparency in working with foundation model developers
Foundation model deployers should work with developers to increase the level of transparency re- garding their models. It is not only in a deployers' interest for developers to share information bilaterally, but also for developers be transparent with the public about the risks and limitations of their models. Deployers themselves can help developers increase transparency by sharing usage statistics.
Deployers should beyond information sharing requests to improve transparency. For example, deployers should aim to negotiate contracts with developers that require developers to publicly share information that is relevant to the developers' customers as well as the broader public, such as information regarding Model Updates, changes in Usage Policy, and Impact. cases where deployers have little leverage over larger developers they should consider partnering with similarly situated deployers to increase their collective bargaining power.
3. Do not put undue trust in opaque foundation models.
Some deployers may take a foundation model from a reputable company at face value, assuming that all of the relevant information about that system is available to deployers and regulators. This could be a serious misjudgment: as our findings show, developers are overwhelmingly not transparent about the development and use their foundation models. Assuming that a model complies with regulatory requirements rogarding information sharing could come with substantial legal risk; for example, if new regulations primarily place information sharing requirements on deployers, they may face legal exposure related to their deployment of opaque foundation models. While developers are presunably more transparent in their relationships with deployers than in their public facing documentation, this is no guarantee that relevant information is shared across the 23 subdomains we identify.
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8.3 Recommendations for policymakers
Policymakers across the United States, China, Canada, the European Union, the United Kingdom, India, Japan, the G7, and many other governments have already taken specific actions on foundation models and generative AI (see Appendix DD. Evidence-driven policy that is grounded in rich and sophisticated under- standing of the current foundation model market is likely to achieve the best outcomes. As a result, our extensive characterization of transparency provides three core insights: (i) what aspects of transparency are present in status quo absent regulatory intervention, (ii) if mandated and enforced, what aspects of trans- parency would change relative to the status quo, and (iii) what substantive requirements beyond transparency would be most appropriate given the newfound transparency? We hope that lawmakers will draw on the information we aggregate in the Foundation Model Transparency Index to better inform policy initiatives. To be clear, our intent. is to not to make claim about whether specific governments should should not regulate foundation models at this time, though some policy intervention is likely needed. Nor is our intent to recommend broad disclosure requirements, which could cause substantial harm if they are implemented without regard for differences in developers' business models and their level of financial resources, or without adequate government support for regulatory compliance. Our view is that a better understanding of the status quo will lead to smarter policy, which leads to the following recommendations.
1. Transparency should be top priority for AI legislation.
Mechanisms to promote transparency should be among the suite of policy tools that lawmakers use to encourage responsible development of foundation models (Engler 2023 Hacker et al.) 2023). Unlike many other policy tools, transparency can be relatively low cost where developers already possess the relevant information, sharing does not require data collection. Another advantage of pro-transparency policies are that they can help solve collective action problems with respect to sharing information with the public. If one developer shares much more information about its foundation model with the public, then it could theoretically be penalized by investors or scrutinized by regulations for having more information about the model's risks and limitations publicly available than its competitors. As a result, developer may be hesitant to be a first mover on transparency if its competitors are steadfast in maintaining opacity. By contrast, if that developer's 's peers must also share information about the risks and limitations of their foundation models, there is much less potential for transparency to represent a competitive disadvantage.
Transparency is a fundamental prerequisite for accountability, robust science, continuous innovation, and effective regulation. With additional information about companies' business practices, the impact of their foundation models, the resources used to build models, and the Al supply chain, governments would be much better positioned to enact comprehensive AI regulations.
Policymakers have responsibility to ensure that the public has adequate information about ex- tremely powerful AI systems that hundreds of millions of people use.
2. Regulators should enforce existing regulation to promote transparency for foundation model developers.
Governments already have substantial authority to require companies to share information about their business practices Ho 2012); Hess (2019); Irion (0022). For example, in recent years data protection authoritics have increased their efforts to regulate the development and use of AI (Zanfir- Fortuna 2023); they should consider using these authorities to solicit additional information from foundation model developers regarding the data they use to build foundation models and the labor that goes into producing that data. Similarly, sectoral regulators should consider scrutinizing the deployment of foundation models within their purview and require transparency where appropriate.
3. Policymakers should be realistic about the limits of transparency.
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Transparency is not an end in itself. While having more information about companies' business practices and the foundation model ecosystem will undoubtedly be helpful, the most significant benefits from transparency will stem from the ways in which it elicits changes in business practices and promotes responsible development and use of foundation models.
Transparency is not viable alternative to substantive change. Some interest groups and policymak- ers have nonetheless pushed for transparency requirements as form of 'light-touch' AI regulation. Rather than mandating that companies change their policies and practices, this approach would merely require some level of information sharing with the government. But transparency is only useful insofar as the information it yields is actionable. Increased transparency can help policy- makers have sufficient information about the state the industry that many governments seek to regulate.
While transparency requirements may appear more feasible and even-handed than other policy interventions in the near lerm, polieymakers should recognize that they are likely insulficient to reduce harm in many areas. Even if companies share more information about the impacts of their models on workers and the environment, that may not lead them to improve working conditions reduce emissions. Policymakers should consider measures beyond transparency requirements in a wide variety of areas while balancing other important equities related to competition and algorithmic justice.
4. Governments should craft policy architecture that enables responsible development of open foundation models, which will in turn promote transparency.
Open foundation models are transparent than closed foundation models, often by significant margin. This means that policymakers with an interest in transparency should be hesitant to impose regulations on foundation model developers or deployers that make it considerably more difficult to build open foundation models. Measures that substantially increase the legal risk of developing open foundation models by holding foundation model developers liable for model outputs or by requiring comprchensive monitoring of downstream use may ultimately undermine transparency.
Pro-competitive policies such those that encourage variety of different business models in the foundation model ecosystem can promote transparency. If there are only few major technology companies that develop flagship foundation models, it will be easier for those companies to circum- vent transparency rules by coordinating their activities. For instance, a handful of major closed developers could agree that a certain level transparency is sufficient to satisfy their goals and to meet regulatory requirements, leading them obfuscate their business practices in similar ways. If the foundation model ecosystem is dominated by a few incumbents, it will also be easier for those incumbents to jointly engage in regulatory capture as there will be no countervailing narrative from other developers in the ecosystem. By contrast, policies that result in diverse array of open and closed foundation model developers could create positive feedback loop for transparency. The higher level of transparency of open developers can help draw attention to the lack of information available about the resources required to build closed foundation models. Some closed developers in this environment may see it as in their interest to share more information about their models in order to engender more trust in their products and services, which can in turn push less transparent closed developers to alter their business practices.
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9 Impact
The Foundation Model Transparency Index characterizes the transparency of foundation model developers at present. While this descriptive work already yields significant insights and value, our ambition for this work is to drive change. In short, our objective is to improve transparency in the foundation model ecosystem: we believe improved transparency will result in better science, more innovation, greater accountability, and ultimately give society greater collective confidence that this promising technology can truly advance the public interest. To achieve these lofty goals requires changing the conduct of powerful organizations. As with many similar efforts to drive change, have conceptualized a specific theory of change and have considered specific limitations and risks of our work. Consistent with the work's ' spirit of transparency, we describe both mabters plainly below.
9.1 Theory of change
Assessment of any kind naturally characterizes the status quo. However, our intent is for our assessment to drive change, especially given that our most fundamental finding is that there is insufficient transparency in the foundation model ecosystem. [Bommasani 2023) argues that evaluation and assessment can drive change if there sufficient uptake: we specifically articulate how assessment can motivate improvement through different forms of uptake.
Assessment motivates improvement. By quantifying transpareney and simultancously scoring many developers, we hope that these organizations will improve their transparency scores over time. We aim to provide a characterization of the status quo that is broadly legible across the ecosystem by directly comparing organizations' transparency scores. Furthermore, specific areas where there is pervasive opacity, or where specific companies are less transparent than their counterparts, are prime targets for public pressure and scrutiny.
With respect to AI companies, we believe that certain leams and employees will play an outsized role in shaping transparency practices. Namely, responsible AI teams along with other teams that address ethics and safety are likely to shape many company-wide practices on transparency, including for their flagship foundation models. For this key group of individuals, we hope the Foundation Model Transparency Index provides a concrete list of indicators to proactively consider in making decisions. At present, we believe that some companies are not transparent about certain issue areas not because of specific countervailing concerns (e.g. profits, privacy, safety) but because they have not explicitly considered whether they should be transparent on this issue. In these cases, we believe the index provides structured, well-argued resource that responsible Al teams directly consider in making decisions around transparency. The index also provides an extensive account of why these specific indicators are valuable, which could help responsible AI teams advocate for greater transparency within their organizations. To be concrete, linking autward-facing transparency reporting with internal-facing company tracking could be a natural outcome where our index could bring about desired change while adding minimal overhead for these companies.
Indexes draw power from their subsequent iterations, allowing for improvements to be clearly measured and acknowledged over time. In the fast-moving foundation model ecosystem, subsequent versions could be motivated by (i) changes in the indicators, (ii) changes in the key companies to assess, (iii) changes in the flagship foundation models of those companies, and (iv) changes in the underlying materials for a specific company. As a result, we believe the maintenance and subsequent versions of the Foundation Model Transparency Index will be necessary for its sustained impact. We have not yet determined an exact cadence and strategy, though we will conduct future versions of the index 6T
A Assessment guides standards and mandates. Fundamentally, the Foundation Model Transparency Index assesses companies metrics of transparency that are selected and evaluated based our judgments experts in this domain. With this in mind, the indicators selected as well the results could directly inform more formal processos. For instance, policymakers around the world are considcring a spectrum of voluntary commitments, industry standards, and mandatory requirements for foundation model developers.
64 htttss::ss///fttt wtw.rrff.saaffrdd.om i for the latest details.
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Many current policy efforts, across the varying levels of voluntary and mandatory requirements, explicitly name transparency as a top-level priority and directly identify specific indicators and subdomains covered by our index (see Appendix D).
Our index is likely to be more comprehensive, more fine-grained, and more empirically grounded than most ongoing policy initiatives. As a result, our index provides direct value to policymakers. In selecting requirements, policymakers can use the index to explore the broader universe of potential transparency and disclosure requirements. In defining requirements, policymakers can use the index to explore specific definitions as well as edge cases that may complicate requirement. And, in ultimately deciding which organizations to regulate and how to enforce regulations, policymakers can look at the current status quo to efficiently allocate resources.
As a brief example, consider the European Parliament's position for the EU AI Act 65 which was adopted by the Parliament on June 14, 2023 by a vote of 499 in favour, 28 against and 93 abstentions Bommasani et al. (2023a) provide an initial analysis of potential compliance with the the Act as proposed in the con- text of foundation model developers. Given this legislative proposal, European lawmakers might recognize that topics related to upstream labor and downstream impact, which are covered in the Foundation Model Transparency Index, are not adequately addressed in the draft AI Act. Policymakers might also acknowledge that requirements to disclose a summary of any copyrighted training data are too vague and a more specific definition, such as the definition we provide in Appendix B may be desirable to improve compliance. And, finally, policymakers might view the results of how open and closed developers fare in deciding which re- quirements are best targeted at which developers along the release spectrum. Overall, much as transparency is instrumental for key societal objectives like public trust, we believe the Foundation Model Transparency Index can be similarly instrumental for key societal processes like sound policy-making.
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9.2 Limitations and risks
Equating transparency and responsibility. Because we foreground transparency in our assessment. of developers and their flagship models, it is likely that some will misinterpret the Foundation Model Trans- parency Index as a mcasure of the responsibility of companics. This is not the case for a number of reasons; most importantly, we award points on the basis of whether developer is transparent about each indicator, not whether it has responsible business practices tied to that indicator. Concretely: if a developer discloses that it pays data laborers just one cent per hour, it would score points on the wages indicator under our methodology, while a developer that pays data laborers $20 an hour but does not make that information publicly available would score no points.
This means that one risk of our approach is that it could incentivize developers to be transparent in per- formative ways that merely increase the amount of information available about their flagship models but do not reflect an effort on the part of the developer to substantively improve its business practíces. Nev- ertheless, we believe that additional information about each of these indicators is an invaluable first step towards understanding how developers build and use foundation models. This will in turn allow many other evaluations of responsible business practices, in which the level of transparency should be but one factor.
Transparency-washiig There is no guarantee that improved transparency of foundation models will result in more responsible development. As critics of transparency-washine have persuasively argued (Zal- nieriute, 2021], major technology companies have used transparency to create the illusion that they are responsible players with the public's best interest at heart. In this way, transparency can be a shield against further scrutiny, helping to convince the public that foundation models are safe and trustworthy when they may not be.
Similarly, companies may use transparency shield against comprehensive regulation. Companies could face substantial costs if they were required to increase pay for data laborers or forego certain risky use cases for their foundation models, leading some to argue that governments should simply require transparency in
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these verticals. However, transparency alone will not change business' fundamental incentives and, if used to water down regulation, can perpetuate harm. Notwithslanding this risk, transparency may be a more appropriate regulatory option for many of the indicators we consider given the early stage of the foundation model ecosystem and the risk that substantive requirements will disproportionately damage small and open developers.
Gaming the index. Moving forward, developers might attempt to game the Foundation Model Trans- parency Index without actually improving transparency. They could do this by clarifying that they do not share information about cortain practicos and giving justification for doing so. Developers might also exploit the fact that indicators are relatively generous, meaning that they could share minor additional infor- mation indicators that are comparatively easy to satísfy without meaningfully improving transparency. Since scores could theoretically be gamed in this way, it is important to consider the Foundation Model Transparency Index in conjunction with other metrics of companies' business practices.
Binary scoring. The fact that each indicator is binary limits the amount of information that each score can reflect when compared with more expressive scoring schemes. For the same indicator, it is often the case that several developers share much less information than others but they all score one point nonetheless as they cross the threshold for receiving points. Conversely, in certain instances developers disclose some information related to a particular indicator but it is insufficient to receive points, yet they are grouped alongside developers who disclose no information whatsoever about an indicator. We attempt to address this limitation by breaking complex indicators into discrete chunks, meaning that each indicator assesses one key dimension of transparency and can more easily be made binary.
The models we assessed are predominantly language models. For the developers we assess, their associated flagship models are predominantly text-to-text language models (8 of the 10). Of the remaining two, only one includes images as an input (GPT-44) and only one outputs images (Stable Diffussin 2). None of the flagship models we considered include modalities beyond text and images, though these modalities may become more common in the coming years. With this in mind, in principle the indicators are chosen and defined in a largely modality-agnostic fashion to facilitate future assessment as the flagship models in the ecosystem diversify in terms of modalities.
Most companies we assessed are headquartered in the U.S. Of the 10 developers we assess, 7 are headquartered in the United States. Although this reflects the disproportionate global reach of U.S. technology companies, there are salient foundation model developers in other parts of the world that we did not assess in this work. For instance, the index excludes foundation model developers in East Asia that we believe are sufficiently important to evaluate, but they often did not share enough information publicly to even attempt evaluation. We also did not consider Falcon-180B from the Technology Innovation Institute in Abu Dhabi 67 as we had alrcady finalized our evaluations when the model was released in September. We hope that researchers will use Foundation Model Transparency Index and our fully transparent methodology to assess the transparency of these developers as well as others around the world.
Low bar for awarding points. We were generally quite generous in the scoring process. When we determined that a developer scored some version of a half-point, we usually rounded up. Since we assess transparency, we award developers points they explicitly disclose that they do not share information about a particular indicator. We also read developers' documents with deference where possible, meaning that we often awarded points where there are grey areas. This means that developers' scores may actually be higher than their documentation warrants in certain cases we had low bar for awarding points many indicators.
Uneven costs. In designing the Index, we score all companies on the same set of indicators. In turn, when companies are compared on the basis of their scores, we recognize that the relative and absolute costs of achieving a particular score may differ greatly across companies. For example, companics with larger legal
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teams more employees dedicated to document specific activities are likely to fare better on the Index given many of the indicators (e.g. company policies, on model documentation) are directly related to the work function of these personnel. Overall, insofar as the Index shapes a company's social status and perception, as well as impacts regulation in the AI industry, the Index may unevenly advantage and disadvantage companies given the underlying disparities in relevant resources like employee headcount. While in some ways this may reflect a fundamental aspect of transparency (i.c. companies require the employees to do the work required to produce and disclose information to be transparent), we express caution in overly focusing the Index results to the detriment of specific companies.
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10 Conclusion
Our rescarch establishes the extent which foundation model developers are transparent, set against the backdrop of decreasing transparency. Our findings show that the status quo is characterized by widespread lack of transparency across developers, with significant unevenness in how individual developers fare and where they have room for improvement. We take this as serious indictment of the overall ecosystem. Transparency is broadly-necessary condition for other more substantive societal progress, and without improvement opaque foundation models are likely to contribute to harm. Foundation models are being developed, deployed, and adopted at a frenetic pace: for this technology to advance the public interest, real change must. be made lo rectify the fundamental lack transparency the ecosystem.
Acknowledgementt.. We thank Alex Engler, Anna Lee Nabors, Anna-Sophis Harling, Arvind Narayanan, Ashwin Ramaswami, Aspen Hopkins, Aviv Ovadya, Benedict Dellot, Christic Lawrence, Connor Dunlop, Conor Griffin, Dan Ho, Dan Jurafsky, Deb Raji, Dilara Soylu, Divyansh Kaushik, Gerard de Graaf, lason Gabriel, Irene Solaiman, John Hewitt, Joslyn Barnhart, Judy Shen, Madhu Srikumar, Marietje Schaake, Markus Anderljung, Mchran Sahami, Neel Guha, Peter Cihon, Peter Henderson, Rebecca Finlay, Rob Reich, Rohan Taori, Rumman Chowdhury, Russell Wald, Seliem El-Sayed, Scth Lazar, Stella Biderman, Steven Cao, Tatsu Hashimoto, Toby Shevlane, Vanessa Parli, Yann Dubois, Yo Shavit, and Zak RogofT for discussions the topics of foundation models, transparency, and/or indexes that informed the Foundation Model Transparency Index. We especially thank Loredana Fattorini for her extensive work on the visuals for this project, as well as Shana Lynch for her work in publicizing this effort.
Foundation Model Developers. We thank the following individuals their respective organizations for their engagement with our effort, including involvement in responding our initial scores on behalf of their organizations. We emphasize that this acknowledgement should not be understood as an endorsement of the Foundation Model Transparency Index by these individuals, but simply that they were involved in our engagement with their organizations.
AI21 Labs. Yoav Shoham
Amazon. Bratin Saha, Vasi Philomin, Atul Deo, Swami Sivasubramanian, Peter Hallinan
Authropic. Jack Clark, Deep Ganguli, Thomas Liao
Cohere, Aidan Gomez, Danielle Smalls, Scraphina Goldfarb- Tarrant, Nick Jakobi, Saurabh Baji
Google. Slav Petrov, James Manyika, Kremena Goranova, Sarah Portik, Alexandra Belias
Hugging Face. Clement Delangue, Meg Mitchell, Yacine Jernite
Inflection. Mustafa Suleyman, Tim Hwang
Meta. Joelle Pineau, Melanie Kambadur, Joe Spisak, Eric Smith, Louis Martin
OpenAL. Miles Brundage, Lama Ahmad
Stability AI. Emad Mostaque, Ben Brooks
Funding. This work supported in part by the 2022 Hoffman-Ver -ee program the Stanford Institute for Human-Centen Artificial Intelligence (HAT) This work supported in part by the AI2050 program at Schmidt Futures (Grant G-22-63429).
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Conflict of Interest. Given the nature of this work (c.g. potential to significantly impact particular companies and shape public opinion), proactively bring attention to any potential conflicts of interest, deliberately taking a more expansive view of conflit of interest to be especially forthcoming.
Betty Xiong is not, and has not, been affiliated with any of the companies evaluated in this effort or any other private sector entities.
Daniel Zhang is not, and has not, been affiliated with any of the companies evaluated in this effort or any other private sector entities.
Kevin Klyman is not, and has not, been affiliated with any of the companies evaluated in this effort or any other private sector entities.
Nestor Maslej is not, and has not, been affiliated with any of the companies evaluated in this effort or any other private sector entities.
Percy Liang was a post-doc at Google (September 2011-August 2012), consultant at Microsoft (May 2018 May 2023), and a co-founder of Together AI (July 2022 present). He is not involved in any other companies.
Rishi Bommasani is not, and has not, been affiliated with any of the companies evaluated in this effort. Rishi is an author of Jernite et al. (2022), as part of the BigScience initiative, that guided the data governance practices for developing BLOOM. As a result, he is also an author on the 350+ author BLOOM paper (Le Scao et al., 2022 that often cited in the scoring of BLOOMZ.
Sayash Kapoor worked at Meta until December 2020. He has not since worked for the company.
Shayne Longpre has three connections to the assessed developers. He has worked as Student Rescarcher at Google Brain in 2022, and is on-going contributor to Cohere For AI, Cohere's non-profit volunteer research organization. Lastly, he was part of the BigScience initialive, where he contributed to BLOOM (Le Scao et al. 2022).
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A Author contributions
This project was a team effort, built on countless contributions from everyone involved. Contributions span the conceplualizatio, design, execution, analysis, and presentation phases, along with the overarching vision, organization, coordination, and leadership. Below, we describe each team member's contribution.
Betty Xiong: Betty contributed the design, exccution, analysis, and presentation.
Daniel Zhang: Daniel contributed to the design, execution, and presentation.
Kevin Klyman: Kevin co-led the project and coordinated nearly all aspects of the initiative. Kevin contributed to the design, execution, analysis, and presentation.
Nestor Maslej: Nestor contributed to the design, execution, analysis, and presentation.
Percy Liang: Percy advised the project, shaping the initial vision. Percy contributed to the conceptual- ization, design, analysis, and presentation along with providing overarching guidance on all stages.
Rishi Bommasani: Rishi led the project, bringing logether the team, providing the initial vision, and coordinating all aspects of the initiative. Rishi contributed the conceptualization, design, execution, analysis, and presentation.
Sayash Kapoor: Sayash contributed to the design, execution, analysis, and presentation.
Shayne Longpre: Shayne contributed to the design, execution, analysis, and presentation.
B Indicators
1. Upstream + Data + Data size
Definition:
For the data used in building the model, is the data size disclosed?
Data size should be reported in appropriate units (e.g. bytes, words, tokens, images, frames) and broken down by modality. Data size should be reported a precision one significant figure (e.g. trillion tokens, 200 thousand images). No form of decomposition into data phases required.
Notes:
References:
Bender & Friedman 2018a), Gebru et al. (2021
2. Upst +
Data +
Data sources
Definition:
For all data used in building the model, are the data sources disclosed?
Notes: To receive this point, meaningful decomposition of sources must be listed an understand- able way (e.g. named URLs/domains/ httpd//annnnnnaaaaee/e////////// "sourced from the Internet" comes from "licensed sources".
providers). It does not suffice to say data is
References:
Gebru et al. [2021). Hutchinson et al. 2021
3. Upstream + Data + Data creators
Definition: For all data used in building the model, is there some characterization of the people who created the data?
While information about data creators may not be easily discernible for some data scraped from the web, the general sources (URLs/ (URLs/domains) should be listed, and, for other data that is bought, licensed, or collected, reasonable attempt at characterizing the underlying people who provided the data is required to receive this point. The relevant properties of people can vary depending on context: for example, relevant properties could include demographic information like fraction of Black individuals contributing to the datasct, goographic information like fraction of European individuals contributing the dataset, language information like fraction of L1 English speakers, occupational information like the fraction of professional artists.
Notes:
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References:
Gebru et al. [2021] Hutchinson et al. 2021
4. Upstream +
Data
Data source selection
Definition: Are the selection protocols for including and excluding data sources disclosed?
Selection protocols refer to procedures used to choose which datasets or subsets of datasets will be used to build model. We will award this point even if the selection protocols are non- exhaustive.
Notes:
References;
Gebru al. [2021) Hutchinson et al. 2021
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5. Upstream -> Data
Data curation
Definition:
For all data sources, are the curation protocols for those data sources disclosed?
Notes:
Curation protocols refer to steps taken to further modify data sources, such as procedures to manage, annotate, and organize data. The aims of curation might include improving the quality, relevance, and representativeness of the data. We will award this point the developer reports that does not perform any further curation beyond the data sources.
References:
Gebru et al. [2021] Hutchinson et al. 2021
6. Upstream + Data
Data augmentation
Definition:
Are any steps the developer takes to augment its data sources disclosed?
Such steps might include augmenting data sources with synthetic data. We will award this point if the developer reports that it does not take any steps to augment its data.
Notes:
References:
Gebru al. [2021). Hutchinson et al. 2002
7. Upstream + Data
Harmful data filtration
Definition: If data is filtered to remove harmful content, is there a description of the associated filter?
Such harmful content might relate violence or child sexual abuse material. We will award this point if the developer reports that it does not. perform any harmful data filtration.
Notes:
References:
Dodge et al. 2021 Longpre et al. 2023
8. Upstream +
Data + Copyrighted data
Definition:
For all data used in building the model, is the associated copyright status disclosed?
Notes: To receive this point, the copyright status (e.g. copyrighted, public domain) must relate lo some decomposition of the data. We will award this point if there some meaningful decomposition of the data, even the decomposition is insufficient to receive the Data Creators point or the disclosure is not comprehensive relative legal copyright standards.
References: Bandy & Vincent 2021 Cooper et al. 2023
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9. Upstream ->
Data
Data license
Definition:
For all data used in building the model, is the associated license status disclosed?
Notes:
To receive this point, the license status must relate to some docomposition of the data. We will award this point if there is some meaningful decomposition of the data, even if the decompasition insufficient to receive the Data Creators point.
References:
Bandy & Vincent 2021 Cooper et al 2023
10. Upstream Data
Personal information in data
Definition: For all data used in building the model, is the inclusion or exclusion of personal infor- mation in that data disclosed?
Notes: To receive this point, the disclosure of personal information must relate to some decompo- sition of the data. We will award this point if there is some meaningful decomposition of the data, even if the decomposition is insufficient to receive the Data Creators point. Additionally, we will award this point if the developer reports the inclusion personal information, independent of if and how they mitigate related privacy concerns.
References:
West
2019),
Brown et al. 20022
11. Upstream
Data labor
Use of human labor
Definition:
Are the phases of the data pipeline where human labor involved disclosed?
Phases of the data pipeline that involve human labor include activities and tasks performed by people to collect, annotate, clean, or validate data. This indicator is inclusive of all data that is created by or on behalf of the developer. We will award this point if the developer gives reasonable best-effort description of the use human labor in their data pipeline.
Notes:
References:
Kittur et al. 2013 Dzieza 2023
12. Upstream Data labor Employment of data laborers
Definition: Is the organization that directly employs the people involved in data labor disclosed for each phase of the data pipeline?
Notes: Phases of the data pipeline that involve human labor include activities and tasks performed by people to collect, annotate, clean, or validate data. This indicator is inclusive of all data that is created by or on behalf of the developer. We will award this point if the developer provides the name the organization that employs data laborers, even other details about the employment relationship are not disclosed.
References:
Kittur et al. 2013
Dzicza 2023
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13. Upstream Data labor Geographic distribution of data laborers
Definition: Is geographic information regarding the people involved data labor disclosed for each phase of the data pipeline?
Notes: This indicator is inclusive of all data that is created by or on behalf of the developer. We will award this point if the developer gives reasonable best-effort. description of the geographic distribution labor at the country-level.
References:
Hao & Seetharaman (2023) Gray & Suri 2019a
14. Upstrcam
Data labor
Wages
Definition:
Are the wages for people who perform data labor disclosed?
This indicator is inclusive of data labor at all points of the model development process, such as training data annotation or rod teaming data used to control the model. We will award this point if the developer reports that it does not compensate workers. For all data that is created by or on behalf of the developer,
Notes:
References: Kittur et al. 2013 Dzieza 2023
15. Upstream Data labor Instructions for creating data
Definition: Are the instructions given to people who perform data labor disclosed?
This indicator is inclusive of all data that is created by behalf of the developer. We will award this point if the developer makes reasonable best-effort attempt to disclose instructions given to people who create data used to build the model for the bulk of the data phases involving human labor.
Notes:
References;
Sambasivan al.
2021 Kittur al. 2013
16. Upstream Data labor - Labor protections
Definition:
Are the labor protections for people who perform data labor disclosed?
This indicator inclusive of data labor at all points of the model development process, such training data, annotation red teaming data used to control the model. Tt. is also inclusive of all data that is created by on behalf of the developer. As an example, labor protections might include protocols to reduce the harm to workers' mental health stemming from exposure to violent content when annotating training data. We will award this point if the developer reports that it does not protect workers or if it does not use data laborers and therefore has no labor protections.
Notes:
References:
Crawford
2021
Gray & Suri 2019a
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17. Upstream Data labor Third party partners
Definition: Are the third parties who were or are involved in the development the model disclosed?
This indicator is inclusive of partnerships that go beyond data labor as there may be third party partners at various stages in the model development process. We will award this point if the developer reports that it was the sole entity involved in the development of the model.
Notes:
References:
Crawford
2021
Gray & Suri 2019a
18. Upstream
Data access
Queryable external data access
Definition:
Are external entities provided with queryable access to the data used to build the model?
Notes: We will award this point for any reasonable mechanism for providing access: direct access to mediated access program where developers inspect requests, etc. Developers may receive this point even if there are rate-limits on the number of queries permitted to an external entity and restrictions on which external entities are given access, insofar as these limits and restrictions are transparent and ensure a reasonable amount of external access. We may accept justifications prohibiting queries of specific parts the data.
the data, interface to query the data, developer-mediatedd
References:
Gebru et al. 2201 Piktus et al. (2023
19. Upstream
Data access
Direct external data access
Definition:
Are external entities provided with direct access to the data used to build the model?
Notes: We will award this point if external entities directly access the data without any form of gating from the developer. With that said, we may award this point if the developer provides justifications for prohibiting access to specific parts of the data or to unauthorized external entities.
References:
Gebru et. al. [2021) Piktus et al. (2023
20. Upst.eem
Compute - Compute usage
Definition: Is the compute required for building the model disclosed?
Compute should be reported in appropriate units, which most often will be floating point operations (FTOPSS). Compute should be reported to precision of one significant figure (e.g. 20a5 FLOPS). We will award this point even if there is decomposition of the reported compute usage into compute phases, but it should be clear whether the reported compute usage is for single model run or includes additional runs, or hyperparameter tuning, or training other models like reward models, or other stops in the model development process that necessitate compute expenditure.
Notes:
References: Henderson et al. (2020) Strubell et al. 2019
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21. Upstream Compute - Development duration
Definition: Is the amount of time required to build the model disclosed?
The continuous duration of time required to build the model should be reported in weeks, days, or hours to a precision of one significant figure (e.g. 3 weeks). No form of decompasition into phases of building the model is required for this indicator, but it should be clear what the duration refers to (e.g. training the model, training and subsequent evaluation and red teaming).
Notes:
References:
Sevilla et al. 2022), Hoffmann et al. 2022b
22. Upstrcam
Compute - Compute hardware
For the primary hardware used to build the model, is the amount and type of hardware disclosed?
Definition:
Notes: In most cases, this indicator will be satisfied by information regarding the number and type of GPUs or TPUs used to train the model. The number of hardware units should be reported to a precision of one significant figure (e.g. 800 NVIDIA H100 GPUs). We will not award this point if (i) the training hardware generally used by the developer is disclosed, but the specific hardware for the given model not, or (ii) the training hardware is disclosed, but the amount of hardware not. We will award this point even if information about the interconnects between hardware units is not disclosed.
References:
Sevilla et al. 2022 Hoffmann et al. 2022b
23. Upstream + Compute + Hardware owner
For the primary hardware used in building the model, is the owner of the hardware disclosed?
Definition:
For example, the hardware owner may be the model developer the case of self-owned cluster, cloud provider like Microsoft Azure, Google Cloud Platform, or Amazon Web Services, national supercomputer. In the event that hardware owned by multiple sources is highly decentralized, will award this point if developer makes reasonable effort to describe the distribution of hardware owners.
Notes:
References:
Sevilla et al. 2022). Hoffmann et al. (002bb)
24. Upstream + Compute + Energy usage
Definition: Is the amount of energy expended in building the model disclosed?
Notes: Energy usage should be reported in appropriate units, which most often will be megawalt- hours (mWh). Energy usage should be reported a precision of one significant figure (e.g. 500 mWh). No form of decomposition into compute phases is required, but it should be clear whether the reported energy usage is for single model run includes additional runs, hyperparameter luning, or training other models like reward models, or other steps in the model development process that necessitate energy usage.
References:
Lacoste al. 2019) Patterson et al.
2021
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25. Upstream Compute - Carbon emissions
Definition: is the amount of carbon emitted (associated with the energy used) building the model disclosed?
Notes: Emissions should be reported in appropriate units, which most often will be tons of carbon dioxide emitted (tCO2). Emissions should be reported to precision of one significant figure (e.g. 500 tCO2). No form of decomposition into compute phases is required, but it should be clear whether the reported emissions for single model run or includes additional runs, or hyperparameter tuning, training of her models like reward models, other steps in the model development process that generate emissions.
References:
Lacoste et al. 2019 Patterson et al. 2021
26. Upstream + Compute + Broader environmental impact
Are any broader environmental impacts from building the model besides carbon emissions disclosed?
Definition:
Notes: While the most direct environmental impact building a foundation model is the energy used and, therefore, the potential carbon emissions, there may be other environmental impacts. For example, these may include the use of other resources such water for cooling data centers metals for producing specialized hardware. We recognize that there docs not exist authoritative or consensus list of broader enviroumental factors. For this reason, we will award this point if there is meaningful, though potentially incomplete, discussion of broader environmental impact.
References: Luccioni & Garnández-Garcia 2023 Strubell et al. 2019
27. Upst.eem
Methods - Model stages
Definition:
Are all stages in the model development process disclosed?
Stages refer to each identifiable step that constitutes a substantive change to the model during the model building process. We recognize that different developers may use different terminology for these stages, conceptualize the stages differently. We will award this point if there is clear and complete description of these slages.
Notes:
References:
Mitchell al. 2019 Chung al. 2022
28. Upstream + Methods - Model objectives
Definition: For all stages that described, is there clear description of the associated learning objectives a clear characterization of the nature of this update to the model?
Notes: We recognize that different developers may use different terminology for these stages, or conceptualize the stages differently. We will award this point if there is clear description of the update to the model related to each stage, whether that is the intent of the stage (e.g. making the model less harmful), a mechanistic characterization (e.g. minimizing specific loss function), or an empirical assessment (e.g. evaluation results conducted before and after the stage).
References: Mitchell al.
2019 Chung et. al. (2022
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29. Upstream Methods Core frameworks
Definition: Are the core frameworks used for model development disclosed?
Examples of core frameworks include Tensorflow, PyTorch, Jax, Hugging Face Transformers, Seqio, T5X, Keras, SciKit, and Triton. If there are siguificant internal frameworks, there should be some description of their function and/or a reasonably similar publicly-available analogue. We recognize that there does not exist an authoritative or consensus list of core frameworks. For this reason, we will award this point there a meaningful, though potentially incomplete, list of major frameworks for the first version of the index.
Notes:
References:
Mitchell et al. 2019), Chung et al. 2022
30. Upstream
Methods - Additional dependencies
Are any dependencies required build the model disclosed besides data, compute, and code?
Definition:
Notes: For example, if the model depends on an external search engine, programmable APIs, or tools, this should be disclosed. We recognize that there is not widespread consensus regarding what constitutes key dependencies beyond the data, compute, and code. We will award this point only if developers give reasonable best-effort description of any additional dependencies make clear that no additional dependencies required.
References:
Lukas et al. 2023 Kim et al. 2023
31. Upstream +
Data Mitigations + Mitigations for privacy
Definition:
Are any steps the developer takes to mitigate the presence of PII in the data disclosed?
Such steps might include identifying personal information in the training data, filtering specific datasets to remove personal information, and reducing the likelihood that models will output personal information. We will award this point if the developer reports that does take steps to mitigate the presence of PIT in the data.
Notes:
References:
Kandpal et al. 2022
Cooper et al. 2023
32. Upstrcam + Data Mitigations - Mitigations for copyright
Definition: Are any steps the developer takes to mitigate the presence of copyrighted information in the data disclosed?
Such steps might include identifying copyrighted data, filtering specific datasets to remove copyrighted data, and reducing the likelihood that models will output copyrighted information. We will award this point if the developer reports that does take steps mitigate the presence of copyrighted information in the data.
Notes:
References:
Bandy & Vincent 2021 Cooper et al. 2023
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33. Model + Model basics Imput modality
Definition: Are the input modalities for the model disclosed?
Notes:
Input modalities refer to the types or formats of information that the model can accept imput. Examples of input modalities include text, image, audio, video, tables, graphs.
References;
Mitchell al. (2019), Crisan et al. (0022)
34. Model - Model basics - Output modality
Definition: Are the output modalities for the model disclosed?
Output modalities refer the types or formats of information that the model can accept output. Examples of oulput modalities include text, image, audio, video, tables, graphs.
Notes:
References:
Mitchell et al. 20199) Crisan et al. 2022
35. Model + Model basics + Model components
Definition:
Are all components of the model disclosed?
Model components refer to distinct and identifiable parts of the model. We recognize that dif- ferent developers may use different terminology for model components, or conceptualize components differently. Examples include: (i) For a text-to-image model, components could refer to a text en- coder and an image encoder, which may have been trained separately. (fi) For angrental-ngmmeneed model, components could refer to separate retriever module.
Noles:
References:
Mitchell et al.
2019
Crisan et al. 2022
36. Model +
Model basics - Model size
Definition:
For all components of the model, is the associated model size disclosed?
Notes: This information should be reported in appropriate units, which generally is the number of model parameters, broken down by named component. Model size should be reported to a precision of one significant figure (e.g. 500 billion parameters for text encoder, 20 billion parameters for image encoder).
References:
Mitchell et al. 2019 Crisan et. al. 2022
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37. Model + Model basics Model architecture
Definition: Is the model architecture disclosed?
Notes:
Model architecture is the overall structure and organization of foundation model, which includes the way in which any disclosed components are inlegrated and how data moves through the model during training or inference. We recognize that different developers may use different terminology for model architecture, or conceptualize the architecture differently. We will award this point for any clear, though potentially incomplete, description of the model architecture.
References: Mitchell et al. 20199) Crisan et al. [2022]
38. Model + Model basics - Centralized model documentation
Definition: Is key information about the model included in a centralized artifact such as a model card?
Notes: We recognize that different developers may share this information through different types of documentation, such as system card or several clearly interrelated documents. We will award this point for the disclosure of any such centralized artifact that provides key information typically included a model card, though the artifact may be longer-form than a standard model card (e.g. technical report).
References:
Mitchell et al. 2019 Crisan et. al. 2022
39. Model +
Model access
External model access protocol
Definition: Is a protocol for granting external entities access the model disclosed?
Notes: A model access protocol refers to the steps, requirements, and considerations involved in granting authorized model access to external entities. We will award this point the developer discloses key details its protocol, including (i) where external entities can request access (e.g. via an access request form); (ii) explicit criteria for selecting external entities; and (iii) transparent decision whether access has been granted within specified, reasonable period of time.
References:
Solaiman
2023 Shevlane
2022
40. Model + Model access
Blackbox external model access
Definition: Is black box model access provided external entities?
Black box model access refers to the ability to query the model with inputs and receive outputs, potentially without further access. Examples of external entities that might be granted access include researchers, third-party auditors, and regulators. We will award this point for any reasonable access level: direct access to the model weights, an interface to query the model, a dexeloper-mediated access program where developers reccive this point if there are rate-limits the number of queries permitted to external entity and restrictions on the external entities that are permitted access, insofar as these limits and restrictions are transparent.
Notes:
inspect requests, etc. Developers may
References: Solaiman
2023 Shevlane (022
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41. Model + Model access Full external model access
Definition: Is full model access provided external entities?
Notes:
Full model access refers to the ability to access the model via the release of model weights. Developers may receive this point even there are some restrictions on the external entities that are permitted access (e.g. geographic restrictions), insofar as these restrictions are transparent (e.g. via some high-level description of who has been granted access to the foundation model).
References:
Solaiman
20023
Shevlane 2022
42. Model + Capabilitios
Capabilities description
Definition:
Are the model's capabilitics described?
Capabilities refer to the specific and distinctive functions that the model can perform. We recognize that different developers may use different terminology for capabilities, conceptualize capabilities differently. We will award this point for any clear, but potentially incomplete, description of the multiple capabilities.
Notes:
References:
Srivastava et al. (022). Liang et al. (022cc
43. Model + Capabilities - Capabilities demonstration
Definition:
Are the model's capabilities demonstrated?
Demonstrations refer to illustrative examples other forms of showing the model's capabil- ities that are legible understandable for the general public, without requiring specific technical expertisc. We recognize that different developers may use different terminology for capabilities, conceptualize capabilities differently. We will award this point for clear demonstrations of multiple capabilitios.
Notes:
References:
Srivastava et al. (022 Liang et al. 2022c
44. Model Capabilities Evaluation of capabilities
Are the model's capabilities rigorously evaluated, with the results of these evaluations reported prior to or concurrent with the initial relcase of the modcl?
Definition:
Notes: Rigorous evaluations refer to precise quantifications of the model's behavior in relation to its capabilities. We recognize that capabilities may not perfectly align with evaluations, and that different developers may associate capabilities with evaluations differently. We will award this point for clear ovaluations of multiple capabilitics. For example, this may include evaluations of world knowledge, reasoning, state tracking or other such proficiencies. it may include the measurement of average performance (e.g. accuracy, F1) on benchmarks for specific tasks (e.g. text summarization, standard broad-coverage benchmarks likely to
image captioning). We note that evaluations suffice for this indicator, though they may not if the model's capabilities presented especially unusual such that standard evaluations will not suffice.
References:
Srivastava et al. (0022) Liang al. (022cc
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45. Model + Capabilities External reproducibility of capabilities evaluation
Definition: Are the evaluations the model's capabilities reproducible by external entities?
For an evaluation to be reproducible by an external entity, we mean that the associated data is either (i) publicly available or (ii) described sufficiently such that reasonable facsimile can be constructed by an external entity. In addition, the evaluation protocol should be sufficiently described such that the evaluation is reproduced, any discrepancies with the developer's results can be resolved. We recognize that there does not exist an authoritative or consensus standard for what required for evaluation to be deemed externally reproducible. Evaluations on standard benchmarks are assumed to be sufficiently reproducible for the purposes of this index. We will award this point for reproducibility of multiple disclosed evaluations. In the event that an evaluation is not reproducible, a justification by the model developer for why it is not possible for the evaluation to be made reproducible may be sufficient to score this point.
Notes:
References:
Kapoor & Narayanan 2023) Liang et al. (002cc)
46. Model -
Capabilitios - Third party capabilitics evaluation
Definition:
Are the model's capabilities evaluated by third parties?
Notes: By third party, we entities that significantly fully independent, of the developer. We will award this point if (i) third party has conducted evaluation of model capabilities, (ii) the results of this evaluation are publicly available, and (iii) these results are disclosed referred to in the developer's materials.
References:
Raji et al. 2022b) Liang el al. 2022c
47. Model - Limitations - Limitations description
Definition:
Are the model's limitations disclosed?
Limitations refer the specific and distinctive functions that the model cannot perform (e.g. the model cannot answer questions about current events it only contains data up to certain time cutoff, the model is not very capable when comes to specific application). We recognize that different developers may use different terminology for limitations, conceptualize limitations differently. We will award this point for any clear, but potentially incomplete, description of multiple limitations.
Notes:
References:
Raji et al. 2022a Liang al. 2022c
48. Model + Limitations Limitations demonstration
Definition: Are the model's limitations demonstrated?
Notes:
Demonstrations refer to illustrative examples other forms of showing the limitations that legible understandable for the general public, without requiring specific technical expertise. We recognize that different developers may use different terminology for linitations, or conceptualize the limitations differently. We will award this point for clear demonstrations of multiple limitations.
References:
Raji et al. 2022a Liang al. 2022c
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49. Model + Limitations Third party evaluation of limitations
Definition:
Can the model's limitations evaluated by third parties?
Notes:
By third parties, we mean entities that are significantly or fully independent of the model developers. In contrast to the third party evaluation indicators for capabilities and risks, we will award this point third party evaluations are possible even if no third party has yet conducted them. Such evaluations are possible if, for example, the model deployed via an API (or with open weights) and there are no restrictions on evaluating limitations (e.g. in the usage policy).
References:
Raji et al. 2022b Liang et al. 2022c
50. Model + Risks
Risks description
Definition:
Are the model's risks disclosed?
Risks refer to possible negative consequences or undesirable outcomes that can arisc from the model's deployment and usage. This indicator requires disclosure of risks that may arise in the event of both (i) intentional (though possibly careless) use, such as bias or hallucinations and (ii) malicious use, such as fraud or disinformation. We recognize that different developers may use different terminology for risks, or conceptualize risks differently. We will award this point for any clear, but potentially incomplete, description of multiple risks.
Notes:
References:
Solaiman et. al. 2023 Weidinger et al.
2021
51. Model +
Risks >
Risks demonstration
Definition:
Are the model's risks demonstrated?
Demonstrations refer to illustrative examples or other forms of showing the risks that are legible or understandable for the general public, without requiring specific technical expertise. This indicator requires demonstration of risks that may arise in the event of both (i) intentional (though possibly careless) use, such as biases or hallucinations and (ii) malicious use, such as fraud disinformation. We recognize that different developers may use different. terminology for risks, conceptualize risks differently. We will award this point for clear demonstrations of multiple risks.
Notes:
References:
Solaiman et al. 20223 Weidinger et al. 2021
52. Model + Risks +
Unintentional harm evaluation
Definition:
Are the model's risks related to unintentional harm rigorously evaluated, with the results of these evaluations reported prior to concurrent with the initial release of the model?
Notes: Rigorous evaluations refer to precise quantifications of the model's behavior relation to
such risks. Unintentional harms include bias, toxicity, and issues relating to fairness, We recognize that unintended harms may not perfectly align with risk evaluations, and that different developers may associate risks with evaluations differently. We will award this point for clear evaluations of multiple such risks. We note that evaluations on standard broad-coverage benchmarks are likely to suffice for this indicator, though they may not if the model's risks related to unintentional harm are presented as especially unusual or severe.
References:
Solaiman et al.
2023 Weidinger et al.
2021
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53. Model + Risks
External reproducibility of unintentional harm evaluation
Are the evaluations of the model's risks related to unintentional harm reproducible by external entities?
Definition:
For an evaluation to be reproducible by an external entity, we mean that the associated data is either (i) publicly available or (ii) described sufficiently such that a reasonable facsimile can be constructed by the external entity. In addition, the evaluation protocol should be sufficiently described such that the evaluation is reproduced, any discrepancies with the doveloper's results can be resolved. We recognize that there does not exist an authoritative consensus standard for standard what is required for evaluation to be deemed externally reproducible. Evaluations benchmarks are assumed to be sufficiently reproducible for the purposes of this index. We will award this point for reproducibility of multiple disclosed evaluations. In the event that an evaluation is not reproducible, a justification by the developer for why it is not possible for the evaluation to be made reproducible may suffice.
Notes:
References:
Kapoor & Narayanan 2023 Weidinger et al. (2021
54. Model + Risks Intentional harm evaluation
Definition: Are the model's risks related to intentional harm rigorously evaluated, with the results of these evaluations reported prior to or concurrent with the initial release of the model?.
Rigorous evaluations refer to precise quantifications of the model's behavior in relation to such risks. Intentional harms include fraud, disinformation, scams, cybersecurity attacks, designing weapons or pathogens, and uses of the model for illegal purposes. We recognize that unintentional harms may not perfectly align with risk evaluations, and that different developers may associate risks with evaluations differently. We will award this point for clear evaluations of multiple such risks. We note that evaluations on standard broad-coverage benchmarks are likely to suffice for this indicator, though they may not if the model's risks related to unintentional harm presented especially unusual or severe.
Notes:
References:
Solaiman el al. (2023 Weidinger et al. 2002
55. Model + Risks -
External reproducibility of intentional harm evaluation
Definition:
Are the evaluations of the model's risks related to intentional harm reproducible by external entities?
For an evaluation be reproducible by an external entity, we mean that the associated data is either (i) publicly available or (ii) described sufficiently such that reasonable facsimile can be constructed by the external entity. In addition, the evaluation protocol should be sufficiently described such that if the evaluation is reproduced, any discrepancies with the developer's results can be resolved. We recognize that there does not exist an authoritative or consensus standard for what is required for an evaluation to be deemed externally reproducible. Evaluations on standard benchmarks are assumed to be sufficiently reproducible for the purposes of this index. We will award this point for reproducibility of multiple disclosed evaluations. In the event that an evaluation is not reproducible, a justification by the model developer for why it is not possible for the evaluation to be made reproducible may suffice.
Notes:
References:
Kapoor Narayanan 2023 Weidinger et al. 2021
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56. Model +
Risks Third party risks evaluation
Definition: Are the model's risks evaluated by third parties?
Notes: By third party, we mean entities that are significantly or fully independent of the devel- oper. A third party risk evaluation might involve the developer allowing a third party to choose a methodology for evaluating risks that differs from that of the developer. We will award this point (i) third party has conducted an evaluation of model risks, (ii) the results of this evaluation are publicly available, and (iii) these results are disclosed or referred to in the developer's materials. If the results are not made public (but disclosed to have been conducted) and/or the results not discoverable in the developer's materials, we will not award this point. We may accept justification from either the third party or the developer for why part of the evaluation is not disclosed in relation
to risks.
References:
Raji et al. (002bb) Weidinger et al. (2021
57. Model -
Model Mitigations -+ Mitigations description
Definition: Are the model mitigations disclosed?
By model mitigations, we refer to interventions implemented by the developer at the level of the model to reduce the likelihood and/or the severity of the model's risks. We recognize that different developers may different terminology for mitigations, or conceptualize mitigations dif- ferently. We will award this point for any clear, but potentially incomplete, description of multiple mitigations associated with the model's risks. Alternatively, we will award this point if the developer reports that does not mitigate risk.
Notes:
References:
Solaiman et al. 2023 Weidinger et al.
2021
58. Model + Model Mitigations + Mitigations demonstration
Definition:
Are the model mitigations demonstrated?
Demonstrations refer to illustrative examples other forms of showing the mitigations that are legible understandable for the general public, without requiring specific technical expertise. We recognize that different developers may use different terminology for miligations, or conceptualize mitigations differently. We will award this point for clear demonstrations of multiple mitigations. We will also award this point if the developer reports that it docs not mitigate the risks associated with the model.
Notes:
References:
Solaiman et al. 22033 Weidinger et al. 2021
59. Model
Model Miligations Mitigations evaluation
Are the model mitigations rigorously evaluated, with the results of these evaluations reported?
Definition:
Rigorous evaluations refer to precise quantifications of the model's behavior in relation to the mitigations associated with its risks. We will award this point for clear evaluations of multiple mitigations.
Notes:
References:
Huang et al. 2023 Weidinger et al. 2021
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60. Model + Model Mitigations - External reproducibility of mitigations evaluation
Definition: Are the model mitigation evaluations reproducible by external entities?
For an evaluation to be reproducible by an external entity, we mean that the associated data is either (i) publicly available or (ii) described sufficiently such that à reasonable facsimile can be constructed by the external entity. addition, the evaluation protocol should be sufficiently described such that the evaluation is reproduced, any discrepancies with the developer's results can be resolved. In the case of mitigations evaluations, this will usually involve details about a comparison to some baseline, which may be different, unmitigated version of the model. We recognize that there does not exist authoritative consensus standard for what is required for an evaluation to be deemed externally reproducible. We will award this point for reproducibility of multiple disclosed evaluations. In the event that an evaluation is not reproducible, justification by the model developer for why it is not possible for the evaluation to be made reproducible may suffice.
Notes:
References:
Kapoor & Narayanan 2023), Weidinger al. (2021
61. Model
Model Mitigations + Third party mitigations evaluation
Definition:
Can the model mitigations be evaluated by third partics?
By third party, mean entities that significantly fully independent of the model developers. This indicator assesses whether it is possible for third parties to assess miligations, which is not restricted to the methods the developer uses to assess mitigations. In contrast to the third party evaluation indicators for capabilities and risks, we will award this point if third party evaluations are possible even if no third party has yet conducted them.
Notes:
References:
Raji et al. 2022b), Weidinger et al. 2021
62. Model
Trustworthiness Trustworthiness evaluation
Definition: Is the trustworthiness the model rigorously evaluated, with the results of these evalu- ations disclosed?
Rigorous evaluations refer to precise quantifications of the model's behavior in relation to its trustworthiness. For example, this may include evaluations of the model's robustness or reliability, its uncortainty, calibration, or causality, or its interpretability or explainability. We recognize that trustworthiness may not perfectly align with evaluations, and that different developers may associate trustworthiness with evaluations differently. We will award this point for clear evaluation of the trustworthiness of the model.
Notes:
References: Brundage et al. (020) Wang et al. 2023a
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63. Model
Trustworthiness
External reproducibility of trustworthiness evaluation
Definition:
Are the trustworthiness evaluations reproducible by external entities?
For an evaluation to be reproducible by an external entity, we mean that the associated data is either (i) publicly available or (ii) described sufficiently such that reasonable facsimile can be constructed by the external entity. addition, the evaluation protocol should be sufficiently described such that the evaluation is reproduced, any discrepancies with the developer's results can be resolved. We recognize that there does not exist an authoritative or consensus standard for standard
Notes:
what required for evaluation to be deemed externally reproducible. Evaluations benchmarks assumed to be sufficiently reproducible for the purposes of this index. We will award this point for reproducibility of at least one evaluation. In the event that an evaluation is not reproducible, we may accept a justification by the model developer for why it is not possible for the evaluation to be made reproducible.
References:
Kapoor & Narayanan 2023 Shneiderman (2220
64. Model + Inference + Inference duration evaluation
Definition: Is the time required for model inference disclosed for a clearly-specified task on a clearly- specified set of hardware?
The duration should be reported in seconds to precision of one significant figure (c.g. 0.002 seconds). We recognize that no established standard exists for the standardized reporting of inference evaluation. Therefore, we permit the developer to specify the task and hardware setup, as long as both are disclosed. The hardware this evaluation need not be the hardware the developer uses for inference if it in fact does any inference itself. For example, the specific task might be generating 100,000 tokens as 5,000 sequences of length 20 and the fixed set hardware might be 8 NVIDIA A100s. The hardware in this evaluation need not be the hardware the developer uses for inference if it in fact does any inference itsclf.
Notes:
References:
Reddi et al. 22000), Narayanan et al. 2023
65. Model + Inference + Inference compute evaluation
Definition: Is the compute usage for model inference disclosed for a clearly-specified task on clearly-specife set of hardware?
Notes: Compute usage for inference should reported FLOPS to precision one significant figure (e.g. 1025 FLOPS). We recognize that no established standard exists for the standard- ized reporting of inference evaluation. Therefore, we permit the developer to specify the task and hardware setup, long both are clear. For example, the specific task might be generating 100k lokens as 5k sequences of length 20 and the fixed set of hardware might be 8 NVIDIA A A100s. The hardware in this evaluation need not be the hardware the developer uses for inference it in fact does any inference itself.
References: Reddi et al. (2020), Narayanan al.
2023
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66. Downstream
Distribution - Release decision-making
Definition: Is the developer's protocol for deciding whether or not to release a model disclosed?
Notes: We recognize that the release of foundation model falls along spectrum, with many forms of partial release, and that different developers may conceptualize release differently. We will award this point for any clear protocol that discusses the decision-making process, including if the protocol more general to the developer rather than the specific foundation model under consideration.
References:
Solaiman
2023
Liang et al. 2022a
67. Downstream
Distribution - Release process
Definition: Is a description of the process of how the model was released disclosed?
A description of the release process might include information about who received access to the model at what stage of the release of the model. For example, a developer might conduct staged release where it releases the model to a select group at first and subsequently makes the model more widely available. We recognize that the release a foundation model falls along a spectrum, with many different forms of release, and that different developers may conceptualize release differently. We will award this point for any detailed discussion of the release process, inchuding if the discussion more general to the developer rather than the specific foundation model under consideration.
Notes:
References:
Solaiman
2023
Liang et. al. 2022a
68. Downstream + Distribution -> Distribution channels
Definition:
Are all distribution channels disclosed?
By distribution channel, we mean any pathway by which the model is made accessible to entities beyond the developer. We recognize that distribution channels may arise without the knowledge the model developer. For example, the weights of a model may be released through one distribution channel and then be distributed through other channels. We will award this point if the developer discloses all of the distribution channels of which it is aware
Notes:
References:
Cobbe et al. 2023 Widder & Wong 2023
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69. Downstream Distribution - Products and services
Definition: Does the developer disclose whether any products and services offered by the developer are dependent on the model?
Notes: We recognize that a developer may provide many products and services that depend on a foundation model or internal derivatives the model. We will award this point for a reasonable best-effort description of any ways the developer makes internal use of the model in its products or services.
References:
Cobbe et al. 2023 Cen et al.
(2023
70. Downstream
Distribution + Detection of machine-generatee content
Definition:
Are any mechanisms for detecting content generated by this model disclosed?
Such a mechanism might include storing copy of all outputs generated by the model to compare against, implementing watermark when generating content using the model, or training a detector post-hoc to identify such content. We will award this point if any such mechanism is disclosed or if the developer reports that it has no such mechanism.
Notes:
References: Kirchenbaner et al. 2023) Kuditipudi et al. 2023
71. Downstream
Distribution -> Model License
Definition: Is a liconse for the model disclosed?
Notes: In the event that licenses are written more generally, it should be clear which assets they apply to. We recognize that different developers may adopt different business models and therefor have different types of model licenses. Examples of model licenses include responsible Al licenses, open-source licenses, and licenses that allow for commercial use.
References:
Pistilli et al. 2023 Chen al. 2023a
72. Downstream
Distribution + Terms of service
Definition:
Are terms of service disclosed for each distribution channel?
Notes: We will award this point if there terms-of-service that appear to apply to the bulk of the model's distribution channels.
References:
Rakova et al. (002)), Liu et al. 20011
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73. Downstream Usage policy Permitted and prohibited users
Definition: Is a description of who can and cannot use the model disclosed?
Notes: Such restrictions may relate to countries (c.g. US-only), organizations (e.g. no competitors). industries (e.g. no weapons industry users) or other relevant factors. These restrictions on users are often contained multiple policies; we group them here for simplicity. We will awarded this point for a clear description permitted, restricted, and prohibited users of the model.
References:
Cohere
[2022]
Meta 20023
74. Downstream
Usage policy - Permitted, restricted, and prohibited uses
Definition:
Are permitted, restricted, and prohibited uses of the model disclosed?
We will award this point if at least two of the following three categories are disclosed: (i) permitted uses, (ii) restricted uses, and (iii) prohibited uses. By restricted uses, we mcan uses that require a higher level of scrutiny (such as permission from or a separate contract with the developer) to be permitted. These uses are generally included an acceptable use policy, model license, or usage policy.
Notes:
References:
Cohere
(2022
Meta 2023
75. Downstream Usage policy
Usage policy enforcement
Definition: Is the enforcement protocol for the usage policy disclosed?
Notes: By enforcement protocol, refer to (i) mechanisms for identifying permitted and prohibited /prohibited reaiiricccccccepopoiiteeeedd uses, (iii) steps the developer takes to enforce its policies related to such uses, and (iv) the developer's procedures for carrying out these steps. We will award this point for reasonable best-effort. attempt to provide the bulk of this information, though one line indicating the developer reserves the right to terminate accounts is insufficient. Alternatively, we will award this point if the developer reports that it does not enforce its usage policy.
users, (ii) mechanisms for identifying permitted/restrictee
References:
Cohere 2022
Meta 2023
76. Downstream + Usage policy + Justification for enforcement action
Definition: Do users receive a justification when they are subject an enforcement action for violating the usage policy?
Notes: For example, does the developer disclose a protocol for telling users which part of the usage policy they violated, when they did so, and what specifically was violative? Enforcement actions refer to measures to limit user's ability
use the model, such as banning user restricting their ability to purchase tokens. We will award this point if the developer discloses that it gives justification for enforcement actions or, alternatively, if it discloses that it does not provide justification for enforcement actions that it does not enforce its usage policy.
References: Cohere (0222)
Meta 2023
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77. Downstream Usage policy
Usage policy violation appeals mechanism
Definition: Is a mechanism for appealing potential usage policy violations disclosed?
Notes:
We will award this point if the developer provides a usage policy violation appeals mechanism, regardless of whether it is provided via a user interface or distribution channel.
References:
Cohere 20022]
Meta 20233
78. Downstream -> Model behavior policy - Permitted, restricted, and prohibited model behaviors
Definition:
Are model behaviors that are permitted, restricted, and prohibited disclosed?
model behavior policy, or developer's policy what the foundation model can and cannot do (e.g. such policy may prohibit model from generating child sexual abuse material). We recognize that different developers may adopt different business models and that some business models may make enforcement of a model behavior policy more or less feasible. We will award this point if at least two of the three categories (i.e. permitted, restricted, and prohibited model behaviors) are disclosed. Alternatively, we will award this point if the developer reports that does not impose any restrictions on its model's behavior.
Notes: We refer policy that includes this information
References:
Reuter & Schulze 20233 et al.
2023
79. Downstream Model behavior policy Model behavior policy enforcement
Definition: Is the enforcement protocol for the model behavior policy disclosed?
By enforcement protocol, we refer to mechanisms for identifying whether model behavior is permitted or prohibited and actions that may arise in the event the model behavior policy is violated. For example, the developer may make updates to the model in response to issues with the model's adherence to the model behavior policy. We will award this point there is a clear description of the enforcement protocol, or if the developer reports that does not enforce its model behavior policy that it has such restrictions the model's behavior.
Notes:
References:
Brundage et al. 2020 Qi et al. 2023
80. Downstream - Model behavior policy + Interoperability of usage and model behavior policies
Definition: Is the way that the usage policy and the model behavior policy interoperate disclosed?
Notes:
For example, if a user attempts to use the model for prohibited use such as spam, how does the model behavior policy apply at all? We will also award this point if the developer reports that does not impose any restrictions on its model's 's behavior the event of usage policy violation.
References:
Reuter & Schulze (2023
Qi ct al. 2023
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81. Downstream User Interface User interaction with AI system
Definition: For distribution channels with user-facing interfaces, are users notified (i) that they are interacting with an AI system, (ii) of the specific foundation model they are interacting with, and (iii) that outputs machine-generatedd!
Notes: A user-facing interface refers to the means by which the user interacts with the foundation model, including how the user can observe outputs from the foundation model and other notifications. We will award this point if, for all distribution channels with user-facing interfaces, the user is provided adequate transparency to the foundation model being distributed and the potential presence of any model outputs.
References:
Wang et al. (003bb, Nakao et al. 2022
82. Downstream + User Interface + Usage disclaimers
Definition: For distribution channels with user-facing interfaces, users provided with disclaimers involving model use?
A user-facing interface refers to the means by which the user interacts with the foundation model, including how the user can observe outputs from the foundation model and other notifications. Usage disclaimers could include information about what constitutes usage policy violations how users should interpret model outputs. We will award this point if, for all distribution channels with user-facing interfaces, the user is provided with usage disclainers.
Notes:
References:
Wang et al. (2023b) Nakao et al.
2022
83. Downstream + User data protection + User data protection policy
Definition:
Are the protocols for how the developer stores, accesses, and shares user data disclosed?
Notes:
We will also award this point if the developer reports that it has no user data protection
policy.
References:
Nissenbaum
20244
King
2020
84. Downstream User data protection - Permitted and prohibited use of user data
Definition:
Are permitted and prohibited uses of user data disclosed?
Developers use user data for range of purposes such building future models, updating existing models, and evaluating both existing and future models. We will award this point if a developer discloses its policy on the use of user data from interactions associated with this model, including both permitted and prohibited uses. This may span different distribution channels if multiple channels supply user data the developer. Alternatively, we will award this point the developer reports it does not impose any limits its use of user data.
Notes:
References:
Nissenbaum 2024). King 2020
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85. Downstream User data protection - Usage data access protocol
Definition: Is a protocol for granting external entities access to usage data disclosed?
Usage data refers to the data created through user interaction with the model, such as user imputs to the model and associated metadata such as the duration of the interaction. A usage data access protocol refers to the steps, requirements, and considerations involved in granting external entities access to usage data; this goes beyond stating the conditions under which related personal information may be shared with external entities. We will award this point for clear description of the usage data access protocol if the developer reports it does not share usage data with external entities.
Notes:
References:
Lapowsky
2018
King 2020
86. Downstream + Model Updates + Versioning protocol
Definition: Is there disclosed version and versioning protocol for the model?
Notes:
By versioning, we mean that each instance of the model is uniquely identified and that the model is guaranteed to not change when referring to fixed version number; alternatively, the
version clearly indicating a specific instance of the model may be able to change by noting that it is the "latest' or 'unstable' version. We recognize that different developers may adopt different versioning practices that may differ from standard semantic versioning practices used elsewhere in software engineering.
References:
Chen et al. (002bb Lam
2020
87. Downstream + Model Updates - Change log
Definition: Is there disclosed change log for the model?
By change log, we mean a description associated with each change to the model (which should be indicated by a change in version number). We recognize that different developers may adopt different practices for change logs that may differ from practices used elsewhere in software engineering. We will award this point if the change log provides clear description of changes that is legible to technical audience.
Notes:
References:
Chen al. (023bb Li al. 2016
88. Downstream + Model Updates + Deprecation policy
Definition: Is there disclosed deprecation policy for the developer?
By deprecation policy, we refer to a description of what it means for a model deprecated and how users should respond the deprecation (e.g. instructions to migrate a newer version). We will award this point for clear disclosure of deprecation policy or if there is risk of deprication (c.g. if the developer openly relcases model weights).
Notes:
References:
Chen et al. (2023b) Haryono et al. 2020
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89. Downstream Feedback + Feedback mechanism
Definition: Is a feedback mechanism disclosed?
Notes:
By feedback mechanism, we refer to mcans for external entities to report feedback or issues that arise in relation to the foundation model. Such entities may include but are not necessarily limited to users. We will award this point if the developer discloses a feedback mechanism that has been implemented.
References:
Bommasani et al. 2023b), Raji et al. (202bb
90. Downstream Feodback - Feedback summary
Definition: Is a report or summary disclosed regarding the feedback the developer received or, alternatively, the way the developer responded that leedback?
Notes: We recognize that there does not cxist an authoritative or consensus standard for what is required in a feedback report. For this reason, we will award this point if there is meaningful, though potentially vague or incomplete, summary of feedback received.
References:
Chen et al. [0021]. Piorkowski et al. (0022]
91. Downstream
Feedback + Government inquiries
Definition: Is summary of government inquiries related to the model received by the developer disclosed?
Notes: Such government inquiries might include requests for user data, requests that certain content be banned, or requests for information about developer's business practioes. We recognize that there does not exist an authoritative or consensus standard for what is required for such a summary of government inquiries. For this reason, we will award this point if (i) there is a meaningful, though potentially vague or incomplete, summary government inquiries, or (ii) summary of government inquiries related user data.
References:
Chou Bommasani et al. 2023b
92. Downstream Impact + Monitoring mechanism
Definition: For each distribution channel, a monitoring mechanism for tracking model use dis- closed?
By monitoring mechanism, we refer to specific protocol for tracking model use that goes beyond an acknowledgement that usage data is collected. We will also award this point for a reasonable best-effort attempt to describe monitoring mechanisms, or if developer discloses that a distribution channel is not monitored.
Notes:
References:
Springer & Whittaker 2018 Bommasani et al. 2023b
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93. Downstream Impact + Downstream applications
Definition: Across forms of downstream use, is the number of applications dependent on the foundation model disclosed?
Notes: We recognize that there does not exist an authoritative or consensus standard for what qualifies as an application. We will award this point if there a meaningful estimate of the number of downstream applications, along with some description of what means for an application to dependent on the model.
References:
Vipra & Korinek 2023 Bommasani et al. (2023b
94. Downstream Impact + Affected market sectors
Definition:
Across all downstream applications, is the fraction applications corresponding to each market sector disclosed?
Notes: By market sector, we refer to an identifiable part of the economy. While established standards exist for describing market sectors, we recognize that developers may provide vague or informal characterizations of market impact. We will award this point if there a meaningful, though potentially vague or incomplete, summary of affected market sectors.
References:
Vipra & Korinek 2023 Bommasani et al. (2033b)
95. Downstream Impact + Affected individuals
Definition: Across all forms of dlonnstremm use, the number of individuals affected by the foun- dation model disclosed?
By affected individuals, we principally mean the number of potential users of applications. We recognize that there does not exist an authoritative or consensus standard for what qualifies as an affected individual. We will award this point there is a meaningful estimate of the number of affected individuals along with clear description of what it means for an individual be affected by the model.
Notes:
References:
Vipra & Korinek 2023 Bommasani et al. 2023b
96. Downstream + Impact - Usage reports
Definition: Is a usage report that gives usage statistics describing the impact of the model on users disclosod?
We recognize that there does not exist an authoritative or consensus standard for what is required a usage report. Usage statistics might include, for example, a description of the major categories of harm that has been caused by use of the model. We will award this point if there is meaningful, though potentially vague incomplete, summary of usage statistics.
Notes:
References:
Brown
2023
Bommasani et al. 2023b
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97. Downstream
Impact + Geographic statistics
Across all forms downstream use, are statistics of model usage across geographies disclosed?
Definition:
We will award this point if there is a meaningful, though potentially incomplete or vague, disclosure geographic usage statistics at the country-level.
Notes:
References:
Brown
2023
Bommasani et al. 2023b
98. Downstream Impact + Redress mechanism
Definition: Is any mechanism to provide redress to users for harm disclosed?
Notes:
We will also award this point if the developer reports it does not have any such redress mechanism.
References:
Vipra & Myers West 2023
Bommasani al. 2023b
99. Downstream + Documentation for Deployers + Centralized documentation for downstream use
Definition: Is documentation for downstream centralized in centralized artifact?
Notes: Centralized documentation for downstream use refers to an artifact, or closely-linked artifacts, that consolidate relevant information for making use of or repurposing the model. Examples of these kinds of artifacts include website with dedicated documentation information, github repository with dedicated documentation information, and ecosystem card. We recognize that different developers may take different approaches to centralizing information. We will award this point if there is a clearly-identifed artifact(s) that contains the majority of substantive information (e.g. capabilitios, limitations, risks, evaluations, distribution channels, model license, usage policies, model behavior policies, feedback and redress mechanismss, dependencies).
References:
Gebru et al. 2021 Mitchell et al. 2019
100. Downstream
Documentation for Deployers
Documentation for responsible downstream
Definition: Is documentation for responsible downstream use disclosed?
Notes: Such documentation might include details on how to adjust API settings to promote respon- sible use, descriptions of how to implement mitigations, also award this point if the developer states that it does not provide any such documentation. For example, the developer might state that the model is offered as is and downstream developers are accountable for using the model responsibly.
guidelines for responsible use. We will
References:
Bommasani et al. 2023b Brown 2023
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C Search protocol
In this section, we outline the search process we used to look for evidence that foundation model developer satisfies our requirements for given indicator.
C.1 General search process
C.1.1
Keyword Definitions
Each item under review has associaled search keywords our GitHub repository
C.1.2 Model-Item Pair Searches
For every model-item pair, conduct a search using the defined keywords within the centralized resources associated with the respective models listed below.
C.1.3 Search Methodology
We employ the following format for every model-itemm-keyword tuple while using Google search, and read through the first 10 search results.
s site: Refer to developer 's website list below] Refer to model name list below
] Enter keyword]
For example, for GPT-4's energy efficiency item, the searches would be:
site: openai com gpt-4 energy
site: openai com gpt-4 efficien
C.1.4 Justification
We note the source (c.g., website, company blog post, paper) for cach picce of evidence that helped confirm an item present, alongside the justification. We link lo an archive.org URL that contains the justification (instead of linking to developers' pages directly), to maintain records.
C.1.5 Avoid Search Personalization
To minimize the influence of personalized search results, we perform all searches in a private or incognito browser tab.
C.1.6
Determination Criteria
If we find one piece of evidence that fully justifies point- or, in cases, points for item, we don't perform other searches.
C.1.7 Distribution Channels
In certain limited cases where the above steps fail to generate any information for indicators related distribution channels, we interact with the developer's intended distribution channel (if disclosed), such as its API its preferred deployment partner's API, or the documentation related this API. We search for the required information via this distribution channel to the extent possible. We also proxies, such model playgrounds, if enterprise access is otherwise required.
https://www https:://wwwgtthhbb github. wwt..///ttaaoooddccr/nnnttttttt
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C.2
Developer website
A121 Labs (Jurassic-2): ai21 com
Amazon (Titan Text): aws a0nazoa.cco/bbbrrrttttttaa.
Anthropic (Claude): anthropic.com
Cohere (Command); cohere. com
Google (PaLM 2): aia.igg. .ooggee
Hugging Face (BLOOMZ): big bigscience. huw.gienae..uggigaaaac.c.o
Inflection (Iuflection-l); inflection. ai
Meta (Llama 2): aa.meta.com com
OpenAl (GPT-4): openai com
Stability (Stable Diffusion 2): stability.co
C.3
Centralized resources for all models
C.3.1
A121 Labs (Jurassic-2)
jurassiic.tech_apper..d
httpe://www..a....oooobool/ulttr
http:://ttaaloo.a22..cooo/ryyyyp
https://docs.. ai2.. ctt. ww122..co/caaaale.......
C.3.2 Amazon (Titan Text)
http://.........aaaaaa...... a istFoundatiionModells
https://docs.at
https://aws. ammpa:o wwtizon.coo/// caa...comaauuuu com/aup/
C.3.3
Anthropic (Claude 2)
http:://iggal.attrrppp..ccoaaapu
httpe://leglllattrpppcc.Coootoo
http:://bggllaaatrppttc..copoopp
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htte:://ww.aabhhpgg....oo////r/s
pdf
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C.3.4
Cohere (Command)
https://bocs.chherr.cco//oooo
attp:///cchrre.co///sutttt///
http:///aaabbbbrr..e.eee.//gggge
http:://cooaerrrccoo///mmssssssu
http:://ccherr..cm/////a///
C.3.5 Google (PaLM 2)
http:///weeaggge...eeeeee.ooooop and fudd_datt_dtg
C.3.6
Faug Hugging Face (BLOOMZ)
http:://arri..orr/b///111111
https://arkv..crr/b///3000055555
htTp:://22xx..orr/bb/2221111000
C.3.7
Inflection (Inflection-1)
httpe//iaaeecciinaa//ee///////BL pdf
https:///afleetiio.aaa/ade
C.3.8
Meta (Llama 2)
http:////shhhhoooooooooooo/////d CARD md
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C.3.9
OpenAl (GPT-4)
httpss//ppeaa..co//eessrrrrg/
https://plattomm.ppaaa.ccoo/o
httpe://opeaas.ccoo//utttttrrrrs
httpss//ssaatus.peenaa.coo///
httpe///geeaaa.ccoopllllleeeeeee
httee://ppeeasscoo//eeaaar///oto
http::/cppenaalcoom/rrrcnngg
https///ppeaa.ccoa/aaaallttgggg2
httpe://ppeeas.ccooooorrttttttte
attee://ppeass.cco//ppppaaaapppt
C.3.10
StabilityAl (Stable Diffusion 2)
httpe://stalltyy..//upppl1 serviice
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D Calls for transparency
In recent years, transparency has been rallying cry for activists, boon to researchers, and tangible first step for governments interested in regulating foundation models. Here we outline some of the salient calls for transparency to illustrate the different stakcholders with an interest. in a more transparent foundation model ecosystem.
Calls for transparency from governments.
A wide varioty of governments have made transparency the development of foundation models a top priority in their wider agenda for Al regulation. In the U.S., the White House has secured voluntary com- mitments from 16 companies that include a commitment 'to publicly reporting their Al systems' capabilities, limitations, and areas of appropriate and inappropriate use' in the form of "transparency reports. 1 The AT Risk Management Framework from the U.S. National Institute for Standards and Technology outlines the U.S. federal government's 's current approach to transparency for foundation models and other AT systems The AI Risk Management Framework states "Trustworthy Al depends upon accountability. Accountability presupposes transparency. Transparency reflects the extent to which information about an AI system and its outputs is available to individuals interacting with such system ... Meaningful transparency provides access to appropriate levels of information based on the stage of the AI lifecyele and tailored to the role knowledge of Al actors individuals interacting with using the AI system.'
The SAFE framework for regulating AI proposed by Senate Majority Leader Schumer aims to ensure that "AI is developed and deployed in a responsible and transparent manner' and to "support US-led innovation in AI technologies including innovation in security, transparency and accountability, also one of the five pillars of the bipartisan framework for a U.S. Al Act proposed by Senators Hawley and Blumenthal; their framework specifically suggests 'requiring transparency from the companies developing and deploying A.I. systems' as it relates to training data, limitations, accuracy, safety, and user interaction with Al system 73 A variely of other draft legislation in the U.S. would require a higher level of transparency for foundation model developers, such as the Algorithmic Accountability Act at the federal level and California's Safety in Artificial Intelligence Act 76
Transparency is
In the EU, transparency and information sharing have become a central focus of the draft EU AT Act. For in- stance, Article 52 of the Act imposes "transparency obligations" for some types of A1 systems. The European Parliament's 's draft of the AI Act included specific obligations for foundation model developers: 'foundation models should have information obligations and prepare all necessary technical documentation for poten- tial downstream providers to be able to comply with their obligations under this Regulation. Generative foundation models should ensure transparency about the fact the content is generated by an Al system, not by humans. 77 Developers of high-risk AI systems may also be required to provide additional transparency about their systems such that deployers have adequate information about risks and how to mitigate them.
China has gone step further, with the central government adopting regulations that impose transparency requirements on foundation model deployers. China's 'Interim Measures for the Management of Generative Artificial Intelligence Services" state that organizations deploying foundation models, including via API, must 'employ effective mcasures to increase transparency in generative AI services. The law further
See Istration wrt.rp/r.etttaaaaaaa.o......aoan Safe http://seaaaaateeee.e.e.eeeaaatr aduin osed and pdf and http:p/:/wwwallllaaaauueeorerrer
to-aaage-------ae-e--eeee--eeeee the
rieks-posed- http://www..a.aaa././//////aaaaa
and
Al-Commitmenter
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er- 20f
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http::/://ttttttcoo//tea https://Aocs.googl.
com/
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specifies that 'providers shall formulate clear, specific, and feasible tagging rules' for data and that 'providers shall establish and complete mechanisms for making complaints and reports, setting up easy complaint and reporting portals, disclosing the process for handling them and the time limits for giving responses."
Many other governments have also highlighted the importance of transparency in the development and use of foundation models. Canada has released a "Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems,' which has been signed by Cohere, the Montreal Institute for Learning Algorithms, and the Vector Institute among other organizations/ 78 Canada's Voluntary Code of Conduct states that signatories commit to achieve transparency such that 'sufficient information is published to allow consumers to make informed decisions and for experts to evaluate whether risks have been adequately addressed." It further specifies that 'developers of advanced generative systems available for public use' are required to 'Publish information on capabilities and limitations of the system... Develop and implement a reliable and freely available method to detect content generated by the system, with a near-term focus on audio-visual content (e.g., watermarking). ... Publish a description of the types of training data used to develop the system, as well as measures taken to identify and miligate risks." Japan is reportedly in the process of adopting its own code of conduct, which may go beyond voluntary commitments/ 79
India's report on 'Impact, Opportunity, and Challenges of Generative AI," coauthored by India's Ministry of Electronics and Information Technology, states that transparency should be a central feature of India's regulatory framework for ensuring responsible use of generative A1 80 The United Arab Emirates' generative Al guide, published by the Office of the Minister for Artificial Intelligence, Digital Economy, and Remote Work Applications, highlights the importance of transparency for generative AI in terms of data protection: "Transparency is crucial to data privacy because it enables individuals to know how their data is collected, processed, and used by organizations. By being transparent, organizations can provide clear and concise information about their data privacy practices, policies, and procedures. [ Data protection authorities around the world are 'de facto regulating generative Al" by using their existing authorities, including those related to information sharing; for example, data protection authoritics in Brazil, Japan, and South Korea launched investigations into OpenAl's ChatGPT in 2023 52
Some governments have highlighted the fact that existing transparency requirements already apply to foun- dation model developers and ought to be enforced such. The UK Competition and Markets Authority notes that transparency requirements are already in place under consumer protection law, and that foun- dation model developers must comply with the transparency provisions of the UK Consumer Rights Act [ The U.S. Federal Trade Commission has stated that 'we take note and can take action if companies aren't upfront about what consumers are buying, who made it, how it was made, or what rights people have in their own creations. ... When offering a generative AT product, [companies] may need to tell customers whether and the extent to which the training data includes copyrighted or otherwise protected material. 484
It is also worth noting that many governments have emphasized the importance of transparency in the development and use of AT systems outside of the context of foundation models. The national AT strategics of Colombia S5 Egypt [66 Indonesia 37 and India 38 highlight the importance of transparency as do the national
AI stralegies of other countries 89
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8
89
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Calls for transparency from international organizations. The UN High Commissioner for Human Rights, Volker Türk, has argued that existing rules for businesses squarely apply to foundation model develop- ers. In a speech in July 2023, Türk stated that generative Al 'companies must live up to their responsibilities to respect human rights in line with the Guiding Principles on Business and Human Rights. ( requiring human rights due diligence, the UN Guiding Principles on Business and Human Rights explicitly refer to transparency as it relates to a company's ' obligation to (i) transparently communicate the human rights impact of its products and (ii) be transparent in administering grievance processes
In addition to
Türk further argued that without adequate guarantees of transparency, generative Al and other types of AI systems should be banned or suspended. He said 'regulations need to require asscssment of the human rights risks and impacts of Al systems before, during, and after their use. Transparency guarantees, inde- pendent oversight, and access to effective remedies are needed, particularly when the State itself is using AI technologies. AT technologies that cannot be operated in compliance with international human rights law must be banned or suspended until such adequate safeguards are in place."
UN Secretary- Gentraa António Guterres has foregrounded transparency well. The UN's digital agenda, summarized in Guterres' Global Digital Compact, makes three key proposals related to transparency: (i) the international community should 'make transparency. fairness and accountability the core of AI governance," (ii) governments should 'consider the adoption of a declaration on data rights that enshrines transparency," and (iii) researchers and companies should be responsible for transparently communicating the risks of Al systems
The G7 Hiroshima AT Process, which was launched in May 2023 and focuses on generative AT, makes 'promotion of transparcncy' one of its core aims 03 A September 2023 joint statement on the Hiroshima AI Process by G7 Digital and Technology Ministers committed the G7 to 'develop guiding principles for organizations developing, deploying, and using advanced Al systems, in particular foundation models and generative AI,' and stated that one such guiding principle could be 'publicly report models' capabilities, limitations and domains of appropriate and inappropriate use, ensuring sufficient transparency: 9
More broadly, international organizations have long noted that transparency is essential for responsible development of AI systems. The OECD AI Principles, adopted in 2019, include transparency as one of five principles for trustworthy AI. The principle on "transparency and explainability" reads: "AI Actors should commit to transparency and responsible disclosure regarding AT systems. To this end, they should provide meaningful information, appropriate to the context, and consistent with the state of art: (i) to foster a general undorstanding of AI systems; (ii) to make stakcholders aware of their interactions with AI systems, including in the workplace; (iii) to enable those affected by an AT system to understand the outcome; and, (iv.) to enable those adversely affected by an AI system to challenge its outcome based on plain and easy-to-understand information on the factors, and the logic that served as the basis for the prediction, recommendation decision." 105 The G20 AI Principles, also adopted in 2019, include this OECD principle on transparency verbatim. 90 A number of other countries have committed to the OECD AT Principles,
including Argentina, Brazil, Egypt, and Singapore 97
igh- agh-commissioner
For inslance, The UN Guiding Principles on Business and Human Rights state, 'The responsibility to respect human rights requires that business enterprises have in place policies and processes through which they can both know and show that they respect human rights in practice. Showing involves communication, providing a measure of transparency and accountability to individuals or groups who may be impacted and to other relevant stakeholders, including investors." See https::/sww.odk ohchr. _en
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n.pdf 2:
ial
96 http:///soocddcoo///aaaaa//11010
97 httpee//acccaa//////apppppppp
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Calls for transparency from foundation model developers. Foundation model developers have also called for greater transperency and touted the benefits of transparency in their own business practices. For example, in June 2022 A121 Labs, Cohere, and OpenAl published "Joint Recommendation for Language Model Deployment' that advocated for increased transparency [Cohere] 2022). Their recommendations stated that developers should "Publish usage guidelines and lerms of use of LLMs ... Document known weaknesses and vulnerabilities, such as bias or ability to produce insecure code ... Documentation should also include model and use-case-ppecific safety best practices.'
Individual developers have highlighted the importance of transparency well. Anthropic ties the importance of transparency to interpretability in its paper on Constitutional Al and in describing the company's 'Core Views on AI Safety" (Bai et al., 2022 98 Inflection prioritizes transparency in its decision-making about the choices it makes with regard to safety. Inflection's Safety Policy states "Safety at its heart a question of values. Companies choose what risks to prioritize, and how to address them. We believe the best principle is to be deliberate about these choices, and transparent with our users about the specific values we build into our Als. We may prioritize values that you disagree with. That's OK. We think that there is room for many perspectives... ... We commit to sharing publicly what positions we aim to take in our Als. 99
OpenAI has argued that transparency can help companics work together to mitigate safety concerns re- garding foundation models 100 Askell et al. (2019) argue "information that companies provide about their intentions and actions how transparent they are can play an important role in whether other compa- nies will cooperate with them." * OpenAl also requires transparency from its suppliers: OpenAl's Supplier Code of Conduct states that "OpenAI expects all Suppliers to adhere to the highest standards of integrity, transparency, honesty, and ethical conduct in all their business dealings.
Cohere states that transparency is important for its responsible development of large language models, noting that it has 'invested in technical and non-technical measures to mitigate potential harm and make our development processes transparent. [T02 Cohere's Usage Guidelines prohibit users from using Cohere's platform for applications with "no transparency. meaning those that 'do not disclose that the content is generated through automated means. 103
Stability AI has called for transparency in connection with its advocacy for open foundation models. In May 2023 report submitted to the U.S. Senate Judiciary Subcommittee on Privacy, Technology, and the Law, Stability AT wrote 'Models like Stable Diffusion and StableI. demonstrate our commitment to AT technology that is transparent, accessible, and human-centric: Rescarchers can 'look under the hood' to verify performance, identify potential risks, and help develop safeguards. Organizations across the public and private sector can customize these models for their own nceds without exposing sensitive data or coding control of their AI capabilities. 1oo The report further argues "These principles can help to advance important policy objectives. Transparent models promote safety and security. ... open models enable the transparent identification, assessment, and management of risks consistent with the National Institute of Standards and Technology AT Risk Management Framework."
We develop open models for transparency.
Hugging Face has also called for transparency as part of its push for open foundation models. In written testimony before the U.S. House Committee on Science, Space, and Technology, Hugging Face CEO Clement Delangue stated "Rigorous documentation practices for Al systems, with transparent reporting that follows well-defined protocols, serves three main goals: incentivizing responsible development; ensuring rescarchers and developers consider values and priorities that may otherwise be overlooked; and creating a paper trail for review. ... transparency from entities about how and where they deploy Al systems to understand what
As the blog post summarizing the paper states, 'Constitutional Al is also helpful for transparency: we can casily specify, inspect, and understand the principles the Al system is following." See http:///www.athrpp..cooo cechrropc..com/inne wtt////meee/aaadeeeooosit.. ut:ooanddtttps://www
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evaluations are most urgently needed. 103 Hugging Face has, along with various partners, released number of artifacts that advance transparency such as tools for exploring datasets (Piktus et al. 2023
In articulating Meta's position with respect to Llama 2, Touvron et al. (2023) state that 'It is important to understand what is in the pretraining data both to increase transparency and to shed light on root causes of potential ream issues, such as potential biases. ... open releases promote transparency and allow more people to access Al tools, democratizing the technology and decentralizing Al expertise." Meta's Responsible Use Guide for Llama 2 encourages downstream developers to 'build transparency and reporting mechanisms in user interactions ... consider ways to provide transparency to end users regarding potential risks and limitations of the system prior to or at the time of user interaction." T08
Amazon makes clear that Iransparency is important with respect to the way in which it communicates its policies to users. Amazon Web Services' Data Privacy Center states that "Our contracts are written in plain, straightforward language to be transparent and help you understand the data privacy protections that we offer. We also provide ongoing data transparency reporting. 117
Google highlights transparency in its Al principles, writing 'For datasets and models, the consistent outcome is to create and publish detailed documentation of datascts and models in the form of structured transparency artifacts known as data and model cards (see the following section for details), which function like nutrition labels, providing information such as the provenance of the data (if a data card) and model performance when tested for fairness (if a model card). 1008 Google's Al principles also detail the "Transparency Artifacts" that Google rescarchers have built, such as Hcalthshects and a Data Cards Playbook.
Microsoft has also produced such artifacts, namely in the form of "Transparency Notes," " which "are intended to help you understand how our Al technology works, the choices system owners can make that influence system performance and behavior, and the importance of thinking about the whole system, including the technology, the people, and the environment. (109
A large number of developers and deployers that we do not assess have also expressed the importance of transparency (Jobin et al.) 2019 Fjeld et al.] 2020; WEF 2023). Notable among them is FleutherAT, a non-profit research group that is leading developer of open foundation models (Skowron & Biderman 2023). Phang et al. (2022) write that 'EleutherAI's approach to rescarch goes beyond transparency: by doing research entirely in public, anyone in the world can observe and contribute al every stage," adding that such public-facing fafin- research fosters highly collaborative, diverse, and innovative research community,
Calls for transparency from researchers, civil society, and labor. While governments and compa- nics have consistently underscored the value of transparency, less powerful actors have banded together to push public and private entities to meaningfully improve transparency along with the business practices that transparency uncovers.
Researchers have driven much of the improvement in transparency for foundation model developers, with innovations like model cards, datasheets, and data statements leading to substantial gains (Mitchell et al., 2018 Gebru et al., 2018 Bender & Friedman 2018a). Some have sought to solidify these improvements in transparency by strengthening the field of algorithmic auditing (Costanza-Chock et al.) 2022). Mozilla's Open Source Audit Tooling project calls for better infrastructure to evaluate and andit Al systems (Rajil 2022). Another proposal to bolster the auditing ecosystem is for governments to conduct third-party audits of Al systems under their existing authority to protect consumers and data subjects (Miller 2021).
Recently, coalitions of researchers led by organizations like LAION have come together to call for greater transparency the foundation model ecosystem (LAION] 2023). In recent congressional hearings, expert testimony has expressed "The Need for Transparency in Artificial Intelligence" (Gregory 2023 Belli
& Gaspar 2023) detail the central importance of transparent foundation models from the perspective of
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experts across Asia, Africa, and Latin America. Other researchers still have argued that transparency, while necessary, is far from sufficient to regulate AT (Hartzog 2023).
Data workers employed as contractors by foundation model developers have also mobilized for increased transparency (Gray & Suri 2019b) 110 For example, in July 2023 members of the African Content Moderators Union filed a petition with Kenya's 8 parliament requesting an investigation into OpenAT, Meta, Google, and other multinational technology companies that employ content moderators in Kenya, Th The petition states that OpenAl used vendor, Sama, to hire the petitioners as contractors who 'trained the ChatGPT algorithm," and alleges that 'the contracts did not sufficiently describe the nature of the job ... we were not properly informed of the nature of the work we would be undertaking." The petition further alleges that although this data labor included 'reading and viewing material that depicted sexual and graphic violence and categorizing it accordingly so that ChatGPT's artificial intelligence could learn it for the purposes of its future interactions with people throughout the contract of training ChatGPT we were not afforded psychosocial support.
The Partnership AI has advocated for transparency with respect to the employment of data enrichment workers, writing 'While shifting how the broader field approaches data enrichment is not a trivial task, increasing transparency regarding current practices and developing more practical guidance can move the field towards improved conditions for data enrichment workers. Greater transparency can help emphasize the central role of data enrichment workers, create the basis for a rich public dialogue of how to improve conditions for workers, and increase confidence in Al models themselves. T13
Civil society groups with range of different focus areas agree that transparency is pressing priority for policymakers and foundation model developers. For instance, 123 civil society organizations, including AccessNow, Algorithm Watch, and the European Center for Not-for-Profit Law, released statement advo- cating for the prioritization of more serious transparency requirements in the EU AI Act TH The statement advocates the inclusion of a 'mandatory impact assessments are a crucial measure to ensure foresight and accountability for potential Al-related harms,' and that "information on all uses of AI systems by public authorities, regardless of the systems' risk level, should be made public the EU database." Additionally, they call for 'an obligation for providers and/or users include information regarding the environmental impact of AI systems," which is not provision in the EU AI Act. Freedom House has also warned that 'AI has allowed governments to refine their online censorship" and threatens to exacerbate the decline in global internet freedom. Al has allowed governments to enhance and refine their online censorship, and foundation models may exacerbate this trend 115 Freedom House points to transparency requirements as a mechanism to identify and combat evolving and subtle censorship pressures.
In October 2023, the U.S. Foderal Trade Commission convened a workshop on the 'Creative Economy and Generative AI,' where creators from across different industries demanded increased transparency. In the words of one participant, "The creative economy only works when the basic tenants of consent, credit, compensation, and transparency are followed. ... Without transparency, we can't even know the extent of
gome policymakers have focused on the importance transparency with respect to data labor. For example, in a letter to the CEOs of major foundation model developers, eight members of the U.S. Congress wrote "Tech companies also must be more transparent about the role data workers play in their AI, so that consumers can make informed choices about the products they use. Unfortunately, many companies have sidestepped these duties, and that must change. ... Please share any plans your company has to be more transparent about the role its data workers play and their working conditions." See htttps///wv markey key. senate. mamaat..gvv hwwp//m/zeedi////ee wetboo//eettertttteo cacrr_tt__rtiiit wwt.rriiiiiial..c... intelligence cotpicce_cmpanieee_eeeeee on dadddaatawwwaaee _aars__ab_____ onditions_- aoditicoss__0911233pd111 091323pdf1. pdr
T+*The Amrccan Content Moderators Union has also sued Meta, alleging that it unlawfully fired workers for their union organizing. See
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11 addition to conducting case study in partnership with Google DeepMind exploring how to increase transparency regarding data labor, the Partnership on Al has separstely published a white paper recommending that developers increase transparency in wages and pay structure for data cnrichment workers. See Stm/htttss///arrttrrhhhpooaa ds/2020/11ccae-stta deepmddddddddddddddde pdf and
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how much of these companies have taken. They took our work and data to train for-profit technologies that then directly compete against us in our own markets using generative media that is meant to mimic us. 116
Despite its limits, transparency is a necessary and broadly popular first step towards accountability for harm caused by AI systems (Kaminski 2020: Bates et al.) 2023). In the context of the rapid rollout of extremely powerful AT systems such as foundation models, transparency all the more urgent. Companies developing and deploying foundation models should heed the call.
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Reviews:
[{"review_id": "lV62msH2XV", "reviewer": "Reviewer_mY2L", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "In my opinion, the development of the index and the corresponding empirical study that scores developers is a laudable effort. The process seems sound and was carried out fairly, the chosen indicators are driven by pragmatism, the results and analyses are comprehensive. My appreciation of the article may seem brief in contrast to the rest of the review, but I do want to underscore that in my opinion this is sound work that is _worthy_ of publication in TMLR.\n\nSome of the weaknesses that I identified:\n\nWith all the effort, the findings are not too surprising and are largely driven by the developers' goals and broader business strategy. Similar is the case with the pairwise developer comparisons. The recommendations, particularly those for developers, could be seen as common-sense and while they broadly follow from the empirical study and analysis, I think they should be reframed, in order to more precisely argue which findings lead to which recommendation.\n\nTwo more abstract points: Firstly, as the paper rightfully mentions, the index could be gamed quite easily by just publishing minimal information that satisfies the indicators (e.g., documenting that some information pertaining to an indicator is not disclosed would technically satisfy the indicator). I wonder if it would make sense to pair the index with a scoring system that awards point if the information provided is actually sufficient, for a more robust take.\n\nSecondly, implicitly or explicitly, the design of the index favours large companies, by equally weighting certain things that big companies have the resources to do (as opposed to non-for-profits or startups) with arguably more fundamental issues regarding transparency. To exemplify, a large company will have the legal \"fire-power\" to draft comprehensive usage policies, TOS etc, allowing them to score easily in this dimensions, while concealing information about the equally-weighted data sources, data licenses and annotation wages. This manifests itself in the overall evaluation scores, where Meta ranks first (perhaps ironically, since Meta was used as the very example of bad transparency practices) and OpenAI ranks third. This could further contribute to the \"regulatory capture\" mentioned on page 56. While it is positive that the paper is concerned with this phenomenon, I think it should acknowledge that The Foundation Index could contribute to it (e.g. in Section 9.2).\n\nTo further emphasise the previous point, the restriction of choosing developers as companies (big tech or startup) seems to imply that developing foundation models is driven by industry. While I understand that the evaluation has been carried out before the publication of AI2's Olmo/Dolma models, it would be interesting to see, how an \"fully open by design\" foundation model published by a non-for-profit organisation, would score on the index, and whether any of the decisions made by AI2 could be used as an example to improve transparency.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces the \"Foundation Model Transparency Index\" - an index of 100 binary indicators to score foundation model developers with regards to their transparency around the process of developing and deploying their foundation models. The indicators are grouped into  three main pillars - upstream, model-level and downstream, which describe the model life cycle from its inception (i.e. data, hardware, compute) over the output artefacts (i.e. model) towards their usage.\n\nThe paper scores ten major foundation model developers on the index and presents and analyses the findings, suggesting that there is significant headroom for all developers to improve their transparency, with upstream transparency (regarding data usage and creation) affected the most.\n\nThe paper continues with different analyses, comparing similarities and contrasting differences between the developers and concludes with a set of comprehensive recommendations for foundation model developers, deployers and policy makers.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "As per the weaknesses outlined above:\n\n- In Section 8, please argue more precisely which findings result in which recommendations\n- In Section 9, please add a discussion on how the index itself could lead to regulatory capture and favour large companies, alongside with an outlook on future work e.g., whether it would make sense to hold different types of developers to different standards (e.g. a fully open enterprise that has limited means of hosting their developed models but instead relies on publishing weights will likely be unable to track its usage) and/or whether to pair it with a scoring system that awards point if the information provided is sufficient to understand the process evaluated by an indicator.\n- Please include AI2 as a case-study of a non-for-profit publisher with their fully open Olmo/Dolma suite or a convincing argument as to why not.\n\n\nMinor remarks:\n\n- page 10 2nd bullet missing a citation\n- page 11 \"vital in fostering improvement over time\" requires citation.\n- page 28 second line in chapter 7: typo\n- detailed figures (pp 32-35, 38) are missing\n- page 37: citation is broken\n- page 23: how are points 1 and 2 measured?", "broader_impact_concerns": "As per my second weaknesses point, I think the paper should acknowledge that indices such as the one proposed here could lead to regulatory capture which stifles competition and actually has the inverse effect of what's intended, by eliminating non-for-profit ad small-scale enterprises. Overall, I don't think this would require a dedicated Broader Impact Statement.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "wJw7mooLQu", "reviewer": "Reviewer_ZEgq", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "## Strengths\n\nThis is a massive and well-organized work. The information that indicators refer to is dominated by that which is deemed important to users of the underlying model, and it is a natural desire that this information should be provided by model developers. The assessment was carried out with the involvement of actual model developers, which is an important achievement in ensuring the effectiveness of the index. By comparing discrepancies between model developers, it is also important that the authors point out that they could probably easily improve some transparency.\n\n## Weaknesses\n\nWhether each model developer can improve a particular indicator depends largely on the model developer's stance. Even if another model developer meets a particular indicator, it is not possible to directly determine whether it is an easy improvement item for another developer. In particular, as shown in the paper, the indicator response trends of open developers and closed developers are very different, which is likely to reflect fundamental differences in positions, and it is thought that it will not be easy to bridge this gap. Of course, it is clear that formulating an index has the effect of motivating model developers to disclose appropriate information. As a more in-depth analysis, it would be appropriate to have a detailed discussion for each model developer about why a particular indicator is as it is at present and whether there is a possibility of improving it in the future. However, this discussion would require deeper insight into each developer than is currently written in the paper. For this reason, it is thought that there is no problem in simply indicating it as a direction for future research. As a note, the rebuttals of each developer are likely to be useful in analyzing this information. Since the authors have such information, in principle, these analyses would be possible. However, it is thought that this paper intentionally avoids detailed analysis on it.\n\nThe recommendations in the paper do not include any suggestions for end users of foundation models or the market itself. It is clear that developers and policymakers have a direct influence on transparency, but their decisions are essentially influenced by public opinion formed by market participants. In my personal experience, foundation model users often only pay attention to its downstream performance. In contrast, they often do not have much interest in whether transparency is guaranteed. It is easy to think that this lack of interest will be a factor that hinders the spread of transparency, and it is necessary to encourage market participants to correctly understand the significance of the index. Also, each recommendation in the paper may be perceived as somewhat subjective, though they looks appropriate in my personal opinion. It would be better to mention which recommendation is effective in improving which indicator.\n\nFinally, although this is mentioned briefly in the limitations, the paper does not provide detailed development procedures for the index and protocols for future improvements. Considering the long-term operation of the index, these are more strategically important than the individual indicators currently adopted, and it would be better to focus on their development.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper describes the authors' overall work around the Foundation Model Transparency Index (referred to as \"the index\" in this review) as of 2023. Reflecting the global trends of extensive application of foundation models, mostly led by development of large language models and image generation models, the authors designed the index to assess whether the developers of the foundation models disclosed appropriate information about/around the model.\n\nThe index consists of 100 indicators, grouped by 3 domains and more specific subdomains. The domains are upstream: how the models are constructed, model: what the models are, and downstream: how the models can be applied, capturing a variety of perspectives of transparency. The index measures the transparency of model developers, not the model itself. Each indicator is a binary classifier having a definition, detailed explanation, and several references to support its importance.\n\nThe authors measured the index of 10 flagship model developers by assessing publicly available information of the models and receiving rebuttals from the developers. Authors then analysed the obtained indicators and provided many insights on them, including overall tendency of each developer, tendency of each domain, comparison between open/closed model developers, and correlation between each specific developer.\n\nGiven the assessment above, the authors finally proposed several recommendations for model developers, deployers, and policymakers, to improve the transparency around the foundation model.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Requests for changes are almost stated in the Weaknesses subsection in the previous response. I think there is no essential flaw on the paper, but the paper contains somewhat less information than I expected.\n\nIn addition, the paper contains many presentation errors, especially misformatting citations and figures, requiring overall verification of its appropriateness.", "broader_impact_concerns": "This paper contains information that will directly affect against every participant of the foundation model market, especially model developers, deployers, and policymakers, but I don't think we need ethical care on the content itself.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "jLGO17Q9Ra", "reviewer": "Reviewer_C82T", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "- Lack of Commercial Consideration: The paper recommends increasing transparency across multiple facets of foundation model development and use, but it does not sufficiently address the commercial implications. Foundation models are highly competitive, with companies investing billions of dollars in developing these technologies. Expecting them to disclose transparency details, especially related to core competitive aspects like data and training processes, might not be practical. The recommendation for transparency levels comparable to research institutions may not align with the commercial reality of the foundation model market, where proprietary technologies offer a significant competitive edge.\n- Inclusion of More Models: The report was completed in September 2023, but since it’s now October 2024, many new LLMs have been developed. While it’s understandable that journal papers take time, I still recommend including more recent models to keep the analysis up to date. Specifically, the report focuses on models from 10 major developers, but this selection may introduce bias, particularly as it seems based on market dominance. Including other models, such as OLMo, LLM360, and models from Chinese developers like Qwen and DeepSeek, would provide a more diverse perspective. Relying solely on developers from established companies could skew the findings, as these companies often prioritize commercial secrecy over transparency. Including models from academic institutions or models explicitly aimed at higher transparency would give a more balanced view.\n- Structural and Formatting Issues (see Requested Changes)", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "## Summary:\nThe paper introduces the Foundation Model Transparency Index, which aims to codify the transparency of foundation models by defining 100 fine-grained indicators cover aspects from the upstream resources used in model development, details about the models themselves, to downstream usage and distribution channels. The author evaluates 10 major foundation model developers, and presents 10 key findings about the current state of transparency in foundation models, emphasizing the lack of disclosure on the downstream impact and how users can seek redress for harm.\n\n## Contributions:\n- The introduction of a comprehensive transparency index that uses 100 detailed indicators.\n- Evaluation of 10 flagship models from leading developers.\n- Presentation of top-level findings on the transparency of foundation model developers.\n## Strengths:\n- The development of a standardized index to assess foundation model transparency is a significant contribution to the field.\n- The detailed indicators across various dimensions (upstream resources, model characteristics, downstream use) provide a well-rounded assessment.\n- The report reveals important gaps in transparency, especially regarding the downstream impact of these models, which is crucial for governance and public accountability.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Page 1 and page 17, footnote exceed the margin.\n- Figures 3, 4, and 5 also exceed the margins; adjusting the table width and reducing space between rows could resolve this.\n- Inconsistent formatting of subsections: Sections 5.1 and 5.2 begin on new pages, while subsections elsewhere in the paper do not. - Standardizing this throughout the paper would improve consistency.\n- There’s odd spacing on page 32.\n- Text following Figures 10, 11, and 12 appears to be misformatted, possibly due to unprocessed LaTeX commands. This needs correction to ensure clarity.\n- Section 3, which briefly introduces the index, feels underdeveloped, given that the core contribution of the paper is the transparency index itself. I will suggest to merge Section 3 with Section 4.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: All reviewers agree that establishing guidelines for transparent foundation model development is essential, and this paper represents a significant contribution.
The Foundation Model Transparency Index is acknowledged as an important milestone in this effort.  

While some concerns were raised, the authors' proposed updates are considered sufficient.
Overall, the reviewers find the study methodologically sound, well-considered in its selection of indicators, and comprehensive in its analyses.

The following is my request to the authors upon the acceptance of the paper:
- Incorporate all suggested changes to reflect the feedback provided.  
- Properly format the paper to align with TMLR guidelines. Specifically:  
  - Ensure that URLs in footnotes do not exceed the page width.  
  - Adjust figures (such as Figures 10 and 11) so that they fit within the TMLR format. Additionally, remove any unintended formatting options that appear in the text (e.g., `margin=1.7cm`).  
- De-anonymize the paper as required for the final submission.
Decision: Accept (poster)